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A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity

Affiliations.

• 1 Centre for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia.
• 2 Centre for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia. Electronic address: [email protected].
• 3 RIADI Laboratory, University of Manouba, Manouba, Tunisia; College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia.
• 4 Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Selangor, Malaysia.
• PMID: 34426171
• DOI: 10.1016/j.compbiomed.2021.104754

Obesity is considered a principal public health concern and ranked as the fifth foremost reason for death globally. Overweight and obesity are one of the main lifestyle illnesses that leads to further health concerns and contributes to numerous chronic diseases, including cancers, diabetes, metabolic syndrome, and cardiovascular diseases. The World Health Organization also predicted that 30% of death in the world will be initiated with lifestyle diseases in 2030 and can be stopped through the suitable identification and addressing of associated risk factors and behavioral involvement policies. Thus, detecting and diagnosing obesity as early as possible is crucial. Therefore, the machine learning approach is a promising solution to early predictions of obesity and the risk of overweight because it can offer quick, immediate, and accurate identification of risk factors and condition likelihoods. The present study conducted a systematic literature review to examine obesity research and machine learning techniques for the prevention and treatment of obesity from 2010 to 2020. Accordingly, 93 papers are identified from the review articles as primary studies from an initial pool of over 700 papers addressing obesity. Consequently, this study initially recognized the significant potential factors that influence and cause adult obesity. Next, the main diseases and health consequences of obesity and overweight are investigated. Ultimately, this study recognized the machine learning methods that can be used for the prediction of obesity. Finally, this study seeks to support decision-makers looking to understand the impact of obesity on health in the general population and identify outcomes that can be used to guide health authorities and public health to further mitigate threats and effectively guide obese people globally.

Keywords: Diseases; Machine learning; Obesity; Overweight; Risk factors.

Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Publication types

• Research Support, Non-U.S. Gov't
• Systematic Review
• Machine Learning
• Metabolic Syndrome*
• Obesity* / epidemiology
• Risk Factors

• Introduction
• Conclusions
• Article Information

Hazard ratio for obesity was modeled according to mean daily step counts and 25th, 50th, and 75th percentile PRS for body mass index. Shaded regions represent 95% CIs. Model is adjusted for age, sex, mean baseline step counts, cancer status, coronary artery disease status, systolic blood pressure, alcohol use, educational level, and a PRS × mean steps interaction term.

Mean daily steps and polygenic risk score (PRS) for higher body mass index are independently associated with hazard for obesity. Hazard ratios model the difference between the 75th and 25th percentiles for continuous variables. CAD indicate coronary artery disease; and SBP, systolic blood pressure.

Each point estimate is indexed to a hazard ratio for obesity of 1.00 (BMI [calculated as weight in kilograms divided by height in meters squared] ≥30). Error bars represent 95% CIs.

eTable. Cumulative Incidence Estimates of Obesity Based on Polygenic Risk Score for Body Mass Index and Mean Daily Steps at 1, 3, and 5 Years

eFigure 1. CONSORT Diagram

eFigure 2. Risk of Incident Obesity Modeled by Mean Daily Step Count and Polygenic Risk Scores Adjusted for Baseline Body Mass Index

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Brittain EL , Han L , Annis J, et al. Physical Activity and Incident Obesity Across the Spectrum of Genetic Risk for Obesity. JAMA Netw Open. 2024;7(3):e243821. doi:10.1001/jamanetworkopen.2024.3821

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Physical Activity and Incident Obesity Across the Spectrum of Genetic Risk for Obesity

• 1 Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
• 2 Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
• 3 Division of Genetic Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
• 4 Vanderbilt Institute of Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee
• 5 Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
• 6 Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee
• 7 Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
• 8 Department of Biomedical Engineering, Vanderbilt University Medical Center, Nashville, Tennessee
• 9 Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
• 10 Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee

Question   Does the degree of physical activity associated with incident obesity vary by genetic risk?

Findings   In this cohort study of 3124 adults, individuals at high genetic risk of obesity needed higher daily step counts to reduce the risk of obesity than those at moderate or low genetic risk.

Meaning   These findings suggest that individualized physical activity recommendations that incorporate genetic background may reduce obesity risk.

Importance   Despite consistent public health recommendations, obesity rates in the US continue to increase. Physical activity recommendations do not account for individual genetic variability, increasing risk of obesity.

Objective   To use activity, clinical, and genetic data from the All of Us Research Program (AoURP) to explore the association of genetic risk of higher body mass index (BMI) with the level of physical activity needed to reduce incident obesity.

Design, Setting, and Participants   In this US population–based retrospective cohort study, participants were enrolled in the AoURP between May 1, 2018, and July 1, 2022. Enrollees in the AoURP who were of European ancestry, owned a personal activity tracking device, and did not have obesity up to 6 months into activity tracking were included in the analysis.

Exposure   Physical activity expressed as daily step counts and a polygenic risk score (PRS) for BMI, calculated as weight in kilograms divided by height in meters squared.

Main Outcome and Measures   Incident obesity (BMI ≥30).

Results   A total of 3124 participants met inclusion criteria. Among 3051 participants with available data, 2216 (73%) were women, and the median age was 52.7 (IQR, 36.4-62.8) years. The total cohort of 3124 participants walked a median of 8326 (IQR, 6499-10 389) steps/d over a median of 5.4 (IQR, 3.4-7.0) years of personal activity tracking. The incidence of obesity over the study period increased from 13% (101 of 781) to 43% (335 of 781) in the lowest and highest PRS quartiles, respectively ( P  = 1.0 × 10 −20 ). The BMI PRS demonstrated an 81% increase in obesity risk ( P  = 3.57 × 10 −20 ) while mean step count demonstrated a 43% reduction ( P  = 5.30 × 10 −12 ) when comparing the 75th and 25th percentiles, respectively. Individuals with a PRS in the 75th percentile would need to walk a mean of 2280 (95% CI, 1680-3310) more steps per day (11 020 total) than those at the 50th percentile to have a comparable risk of obesity. To have a comparable risk of obesity to individuals at the 25th percentile of PRS, those at the 75th percentile with a baseline BMI of 22 would need to walk an additional 3460 steps/d; with a baseline BMI of 24, an additional 4430 steps/d; with a baseline BMI of 26, an additional 5380 steps/d; and with a baseline BMI of 28, an additional 6350 steps/d.

Conclusions and Relevance   In this cohort study, the association between daily step count and obesity risk across genetic background and baseline BMI were quantified. Population-based recommendations may underestimate physical activity needed to prevent obesity among those at high genetic risk.

In 2000, the World Health Organization declared obesity the greatest threat to the health of Westernized nations. 1 In the US, obesity accounts for over 400 000 deaths per year and affects nearly 40% of the adult population. Despite the modifiable nature of obesity through diet, exercise, and pharmacotherapy, rates have continued to increase.

Physical activity recommendations are a crucial component of public health guidelines for maintaining a healthy weight, with increased physical activity being associated with a reduced risk of obesity. 2 - 4 Fitness trackers and wearable devices have provided an objective means to capture physical activity, and their use may be associated with weight loss. 5 Prior work leveraging these devices has suggested that taking around 8000 steps/d substantially mitigates risk of obesity. 3 , 4 However, current recommendations around physical activity do not take into account other contributors such as caloric intake, energy expenditure, or genetic background, likely leading to less effective prevention of obesity for many people. 6

Obesity has a substantial genetic contribution, with heritability estimates ranging from 40% to 70%. 7 , 8 Prior studies 9 - 11 have shown an inverse association between genetic risk and physical activity with obesity, whereby increasing physical activity can help mitigate higher genetic risk for obesity. These results have implications for physical activity recommendations on an individual level. Most of the prior work 9 - 11 focused on a narrow set of obesity-associated variants or genes and relied on self-reported physical activity, and more recent work using wearable devices has been limited to 7 days of physical activity measurements. 12 Longer-term capture in large populations will be required to accurately estimate differences in physical activity needed to prevent incident obesity.

We used longitudinal activity monitoring and genome sequencing data from the All of Us Research Program (AoURP) to quantify the combined association of genetic risk for body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) and physical activity with the risk of incident obesity. Activity monitoring was quantified as daily step counts obtained from fitness tracking devices. Genetic risk was quantified by using a polygenic risk score (PRS) from a large-scale genomewide association study (GWAS) of BMI. 13 We quantified the mean daily step count needed to overcome genetic risk for increased BMI. These findings represent an initial step toward personalized exercise recommendations that integrate genetic information.

Details on the design and execution of the AoURP have been published previously. 14 The present study used AoURP Controlled Tier dataset, version 7 (C2022Q4R9), with data from participants enrolled between May 1, 2018, and July 1, 2022. Participants who provided informed consent could share data from their own activity tracking devices from the time their accounts were first created, which may precede the enrollment date in AoURP. We followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline. In this study, only the authorized authors who completed All of Us Responsible Conduct of Research training accessed the deidentified data from the Researcher Workbench (a secured cloud-based platform). Since the authors were not directly involved with the participants, institutional review board review was exempted in compliance with AoURP policy.

Activity tracking data for this study came from the Bring Your Own Device program that allowed individuals who already owned a tracking device (Fitbit, Inc) to consent to link their activity data with other data in the AoURP. By registering their personal device on the AoURP patient portal, patients could share all activity data collected since the creation of their personal device account. For many participants, this allowed us to examine fitness activity data collected prior to enrollment in the AoURP. Activity data in AoURP are reported as daily step counts. We excluded days with fewer than 10 hours of wear time to enrich our cohort for individuals with consistently high wear time. The initial personal activity device cohort consisted of 12 766 individuals. Consistent with our prior data curation approach, days with less than 10 hours of wear time, less than 100 steps, or greater than 45 000 steps or for which the participant was younger than 18 years were removed. For time-varying analyses, mean daily steps were calculated on a monthly basis for each participant. Months with fewer than 15 valid days of monitoring were removed.

The analytic cohort included only individuals with a BMI of less than 30 at the time activity monitoring began. The primary outcome was incident obesity, defined as a BMI of 30 or greater documented in the medical record at least 6 months after initiation of activity monitoring. The latter stipulation reduced the likelihood that having obesity predated the beginning of monitoring but had not yet been clinically documented. We extracted BMI values and clinical characteristics from longitudinal electronic health records (EHRs) for the consenting participants who were associated with a health care provider organization funded by the AoURP. The EHR data have been standardized using the Observational Medical Outcomes Partnership Common Data Model. 15 In the AoURP, upon consent, participants are asked to complete the Basics survey, in which they may self-report demographic characteristics such as race, ethnicity, and sex at birth.

We filtered the data to include only biallelic, autosomal single-nucleotide variants (SNVs) that had passed AoURP initial quality control. 16 We then removed duplicate-position SNVs and kept only individual genotypes with a genotype quality greater than 20. We further filtered the SNVs based on their Hardy-Weinberg equilibrium P value (>1.0 × 10 −15 ) and missing rate (<5%) across all samples. Next, we divided the samples into 6 groups (Admixed American, African, East Asian, European, Middle Eastern, and South Asian) based on their estimated ancestral populations 16 , 17 and further filtered the SNVs within each population based on minor allele frequency (MAF) (>0.01), missing rate (<0.02), and Hardy-Weinberg equilibrium P value (>1.0 × 10 −6 ). The SNVs were mapped from Genome Reference Consortium Human Build 38 with coordinates to Build 37. Because the existing PRS models have limited transferability across ancestry groups and to ensure appropriate power of the subsequent PRS analysis, we limited our analysis to the populations who had a sample size of greater than 500, resulting in 5964 participants of European ancestry with 5 515 802 common SNVs for analysis.

To generate principal components, we excluded the regions with high linkage disequilibrium, including chr5:44-51.5 megabase (Mb), chr6:25-33.5 Mb, chr8:8-12 Mb, and chr11:45-57 Mb. We then pruned the remaining SNVs using PLINK, version 1.9 (Harvard University), pairwise independence function with 1-kilobase window shifted by 50 base pairs and requiring r 2 < 0.05 between any pair, resulting in 100 983 SNPs for further analysis. 18 Principal component analysis was run using PLINK, version 1.9. The European ancestry linkage disequilibrium reference panel from the 1000 Genomes Project phase 3 was downloaded, and nonambiguous SNPs with MAF greater than 0.01 were kept in the largest European ancestry GWAS summary statistics of BMI. 13 We manually harmonized the strand-flipping SNPs among the SNP information file, GWAS summary statistics files, and the European ancestry PLINK extended map files (.bim).

We used PRS–continuous shrinkage to infer posterior SNP effect sizes under continuous shrinkage priors with a scaling parameter set to 0.01, reflecting the polygenic architecture of BMI. GWAS summary statistics of BMI measured in 681 275 individuals of European ancestry was used to estimate the SNP weights. 19 The scoring command in PLINK, version 1.9, was used to produce the genomewide scores of the AoURP European individuals with their quality-controlled SNP genotype data and these derived SNP weights. 20 Finally, by using the genomewide scores as the dependent variable and the 10 principal components as the independent variable, we performed linear regression, and the obtained residuals were kept for the subsequent analysis. To check the performance of the PRS estimate, we first fit a generalized regression model with obesity status as the dependent variable and the PRS as the independent variable with age, sex, and the top 10 principal components of genetic ancestry as covariates. We then built a subset logistic regression model, which only uses the same set of covariates. By comparing the full model with the subset model, we measured the incremental Nagelkerke R 2 value to quantify how much variance in obesity status was explained by the PRS.

Differences in clinical characteristics across PRS quartiles were assessed using the Wilcoxon rank sum or Kruskal-Wallis test for continuous variables and the Pearson χ 2 test for categorical variables. Cox proportional hazards regression models were used to examine the association among daily step count (considered as a time-varying variable), PRS, and the time to event for obesity, adjusting for age, sex, mean baseline step counts, cancer status, coronary artery disease status, systolic blood pressure, alcohol use, educational level, and interaction term of PRS × mean steps. We presented these results stratified by baseline BMI and provided a model including baseline BMI in eFigure 2 in Supplement 1 as a secondary analysis due to collinearity between BMI and PRS.

Cox proportional hazards regression models were fit on a multiply imputed dataset. Multiple imputation was performed for baseline BMI, alcohol use, educational status, systolic blood pressure, and smoking status using bootstrap and predictive mean matching with the aregImpute function in the Hmisc package of R, version 4.2.2 (R Project for Statistical Computing). Continuous variables were modeled as restricted cubic splines with 3 knots, unless the nonlinear term was not significant, in which case it was modeled as a linear term. Fits and predictions of the Cox proportional hazards regression models were obtained using the rms package in R, version 4.2.2. The Cox proportional hazards regression assumptions were checked using the cox.zph function from the survival package in R, version 4.2.2.

To identify the combinations of PRS and mean daily step counts associated with a hazard ratio (HR) of 1.00, we used a 100-knot spline function to fit the Cox proportional hazards regression ratio model estimations across a range of mean daily step counts for each PRS percentile. We then computed the inverse of the fitted spline function to determine the mean daily step count where the HR equals 1.00 for each PRS percentile. We repeated this process for multiple PRS percentiles to generate a plot of mean daily step counts as a function of PRS percentiles where the HR was 1.00. To estimate the uncertainty around these estimations, we applied a similar spline function to the upper and lower estimated 95% CIs of the Cox proportional hazards regression model to find the 95% CIs for the estimated mean daily step counts at each PRS percentile. Two-sided P < .05 indicated statistical significance.

We identified 3124 participants of European ancestry without obesity at baseline who agreed to link their personal activity data and EHR data and had available genome sequencing. Among those with available data, 2216 of 3051 (73%) were women and 835 of 3051 (27%) were men, and the median age was 52.7 (IQR, 36.4-62.8) years. In terms of race and ethnicity, 2958 participants (95%) were White compared with 141 participants (5%) who were of other race or ethnicity (which may include Asian, Black or African American, Middle Eastern or North African, Native Hawaiian or Other Pacific Islander, multiple races or ethnicities, and unknown race or ethnicity) ( Table ). The analytic sample was restricted to individuals assigned European ancestry based on the All of Us Genomic Research Data Quality Report. 16 A study flowchart detailing the creation of the analytic dataset is provided in eFigure 1 in Supplement 1 . The BMI-based PRS explained 8.3% of the phenotypic variation in obesity (β = 1.76; P  = 2 × 10 −16 ). The median follow-up time was 5.4 (IQR, 3.4-7.0) years and participants walked a median of 8326 (IQR, 6499-10 389) steps/d. The incidence of obesity over the study period was 13% (101 of 781 participants) in the lowest PRS quartile and 43% (335 of 781 participants) in the highest PRS quartile ( P  = 1.0 × 10 −20 ). We observed a decrease in median daily steps when moving from lowest (8599 [IQR, 6751-10 768]) to highest (8115 [IQR, 6340-10 187]) PRS quartile ( P  = .01).

We next modeled obesity risk stratified by PRS percentile with the 50th percentile indexed to an HR for obesity of 1.00 ( Figure 1 ). The association between PRS and incident obesity was direct ( P  = .001) and linear (chunk test for nonlinearity was nonsignificant [ P  = .07]). The PRS and mean daily step count were both independently associated with obesity risk ( Figure 2 ). The 75th percentile BMI PRS demonstrated an 81% increase in obesity risk (HR, 1.81 [95% CI, 1.59-2.05]; P  = 3.57 × 10 −20 ) when compared with the 25th percentile BMI PRS, whereas the 75th percentile median step count demonstrated a 43% reduction in obesity risk (HR, 0.57 [95% CI, 0.49-0.67]; P  = 5.30 × 10 −12 ) when compared with the 25th percentile step count. The PRS × mean steps interaction term was not significant (χ 2 = 1.98; P  = .37).

Individuals with a PRS at the 75th percentile would need to walk a mean of 2280 (95% CI, 1680-3310) more steps per day (11 020 total) than those at the 50th percentile to reduce the HR for obesity to 1.00 ( Figure 1 ). Conversely, those in the 25th percentile PRS could reach an HR of 1.00 by walking a mean of 3660 (95% CI, 2180-8740) fewer steps than those at the 50th percentile PRS. When assuming a median daily step count of 8740 (cohort median), those in the 75th percentile PRS had an HR for obesity of 1.33 (95% CI, 1.25-1.41), whereas those at the 25th percentile PRS had an obesity HR of 0.74 (95% CI, 0.69-0.79).

The mean daily step count required to achieve an HR for obesity of 1.00 across the full PRS spectrum and stratified by baseline BMI is shown in Figure 3 . To reach an HR of 1.00 for obesity, when stratified by baseline BMI of 22, individuals at the 50th percentile PRS would need to achieve a mean daily step count of 3290 (additional 3460 steps/d); for a baseline BMI of 24, a mean daily step count of 7590 (additional 4430 steps/d); for a baseline BMI of 26, a mean daily step count of 11 890 (additional 5380 steps/d); and for a baseline BMI of 28, a mean daily step count of 16 190 (additional 6350 steps/d).

When adding baseline BMI to the full Cox proportional hazards regression model, daily step count and BMI PRS both remain associated with obesity risk. When comparing individuals at the 75th percentile with those at the 25th percentile, the BMI PRS is associated with a 61% increased risk of obesity (HR, 1.61 [95% CI, 1.45-1.78]). Similarly, when comparing the 75th with the 25th percentiles, daily step count was associated with a 38% lower risk of obesity (HR, 0.62 [95% CI, 0.53-0.72]) (eFigure 2 in Supplement 1 ).

The cumulative incidence of obesity increases over time and with fewer daily steps and higher PRS. The cumulative incidence of obesity would be 2.9% at the 25th percentile, 3.9% at the 50th percentile, and 5.2% at the 75th percentile for PRS in year 1; 10.5% at the 25th percentile, 14.0% at the 50th percentile, and 18.2% at the 75th percentile for PRS in year 3; and 18.5% at the 25th percentile, 24.3% at the 50th percentile, and 30.9% at the 75th percentile for PRS in year 5 ( Figure 4 ). The eTable in Supplement 1 models the expected cumulative incidence of obesity at 1, 3, and 5 years based on PRS and assumed mean daily steps of 7500, 10 000, and 12 500.

We examined the combined association of daily step counts and genetic risk for increased BMI with the incidence of obesity in a large national sample with genome sequencing and long-term activity monitoring data. Lower daily step counts and higher BMI PRS were both independently associated with increased risk of obesity. As the PRS increased, the number of daily steps associated with lower risk of obesity also increased. By combining these data sources, we derived an estimate of the daily step count needed to reduce the risk of obesity based on an individual’s genetic background. Importantly, our findings suggest that genetic risk for obesity is not deterministic but can be overcome by increasing physical activity.

Our findings align with those of prior literature 9 indicating that engaging in physical activity can mitigate genetic obesity risk and highlight the importance of genetic background for individual health and wellness. Using the data from a large population-based sample, Li et al 9 characterized obesity risk by genotyping 12 susceptibility loci and found that higher self-reported physical activity was associated with a 40% reduction in genetic predisposition to obesity. Our study extends these results in 2 important ways. First, we leveraged objectively measured longitudinal activity data from commercial devices to focus on physical activity prior to and leading up to a diagnosis of obesity. Second, we used a more comprehensive genomewide risk assessment in the form of a PRS. Our results indicate that daily step count recommendations to reduce obesity risk may be personalized based on an individual’s genetic background. For instance, individuals with higher genetic risk (ie, 75th percentile PRS) would need to walk a mean of 2280 more steps per day than those at the 50th percentile of genetic risk to have a comparable risk of obesity.

These results suggest that population-based recommendations that do not account for genetic background may not accurately represent the amount of physical activity needed to reduce the risk of obesity. Population-based exercise recommendations may overestimate or underestimate physical activity needs, depending on one’s genetic background. Underestimation of physical activity required to reduce obesity risk has the potential to be particularly detrimental to public health efforts to reduce weight-related morbidity. As such, integration of activity and genetic data could facilitate personalized activity recommendations that account for an individual’s genetic profile. The widespread use of wearable devices and the increasing demand for genetic information from both clinical and direct-to-consumer sources may soon permit testing the value of personalized activity recommendations. Efforts to integrate wearable devices and genomic data into the EHR further support the potential future clinical utility of merging these data sources to personalize lifestyle recommendations. Thus, our findings support the need for a prospective trial investigating the impact of tailoring step counts by genetic risk on chronic disease outcomes.

The most important limitation of this work is the lack of diversity and inclusion only of individuals with European ancestry. These findings will need validation in a more diverse population. Our cohort only included individuals who already owned a fitness tracking device and agreed to link their activity data to the AoURP dataset, which may not be generalizable to other populations. We cannot account for unmeasured confounding, and the potential for reverse causation still exists. We attempted to diminish the latter concern by excluding prevalent obesity and incident cases within the first 6 months of monitoring. Genetic risk was simplified to be specific to increased BMI; however, genetic risk for other cardiometabolic conditions could also inform obesity risk. Nongenetic factors that contribute to obesity risk such as dietary patterns were not available, reducing the explanatory power of the model. It is unlikely that the widespread use of drug classes targeting weight loss affects the generalizability of our results, because such drugs are rarely prescribed for obesity prevention, and our study focused on individuals who were not obese at baseline. Indeed, less than 0.5% of our cohort was exposed to a medication class targeting weight loss (phentermine, orlistat, or glucagonlike peptide-1 receptor agonists) prior to incident obesity or censoring. Finally, some fitness activity tracking devices may not capture nonambulatory activity as well as triaxial accelerometers.

This cohort study used longitudinal activity data from commercial wearable devices, genome sequencing, and clinical data to support the notion that higher daily step counts can mitigate genetic risk for obesity. These results have important clinical and public health implications and may offer a novel strategy for addressing the obesity epidemic by informing activity recommendations that incorporate genetic information.

Accepted for Publication: January 30, 2024.

Published: March 27, 2024. doi:10.1001/jamanetworkopen.2024.3821

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Brittain EL et al. JAMA Network Open .

Corresponding Author: Evan L. Brittain, MD, MSc ( [email protected] ) and Douglas M. Ruderfer, PhD ( [email protected] ), Vanderbilt University Medical Center, 2525 West End Ave, Suite 300A, Nashville, TN 37203.

Author Contributions: Drs Brittain and Ruderfer had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Brittain, Annis, Master, Roden, Ruderfer.

Acquisition, analysis, or interpretation of data: Brittain, Han, Annis, Master, Hughes, Harris, Ruderfer.

Drafting of the manuscript: Brittain, Han, Annis, Master, Ruderfer.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Brittain, Han, Annis, Master.

Obtained funding: Brittain, Harris.

Administrative, technical, or material support: Brittain, Annis, Master, Roden.

Supervision: Brittain, Ruderfer.

Conflict of Interest Disclosures: Dr Brittain reported receiving a gift from Google LLC during the conduct of the study. Dr Ruderfer reported serving on the advisory board of Illumina Inc and Alkermes PLC and receiving grant funding from PTC Therapeutics outside the submitted work. No other disclosures were reported.

Funding/Support: The All of Us Research Program is supported by grants 1 OT2 OD026549, 1 OT2 OD026554, 1 OT2 OD026557, 1 OT2 OD026556, 1 OT2 OD026550, 1 OT2 OD 026552, 1 OT2 OD026553, 1 OT2 OD026548, 1 OT2 OD026551, 1 OT2 OD026555, IAA AOD21037, AOD22003, AOD16037, and AOD21041 (regional medical centers); grant HHSN 263201600085U (federally qualified health centers); grant U2C OD023196 (data and research center); 1 U24 OD023121 (Biobank); U24 OD023176 (participant center); U24 OD023163 (participant technology systems center); grants 3 OT2 OD023205 and 3 OT2 OD023206 (communications and engagement); and grants 1 OT2 OD025277, 3 OT2 OD025315, 1 OT2 OD025337, and 1 OT2 OD025276 (community partners) from the National Institutes of Health (NIH). This study is also supported by grants R01 HL146588 (Dr Brittain), R61 HL158941 (Dr Brittain), and R21 HL172038 (Drs Brittain and Ruderfer) from the NIH.

Role of the Funder/Sponsor: The NIH had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

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• Published: 14 April 2004

Obesity: An overview on its current perspectives and treatment options

• Srinivas Nammi 1 , 3 ,
• Saisudha Koka 1 ,
• Krishna M Chinnala 2 &
• Krishna M Boini 1 , 3  

Nutrition Journal volume  3 , Article number:  3 ( 2004 ) Cite this article

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Obesity is a multi-factorial disorder, which is often associated with many other significant diseases such as diabetes, hypertension and other cardiovascular diseases, osteoarthritis and certain cancers. The management of obesity will therefore require a comprehensive range of strategies focussing on those with existing weight problems and also on those at high risk of developing obesity. Hence, prevention of obesity during childhood should be considered a priority, as there is a risk of persistence to adulthood. This article highlights various preventive aspects and treatment procedures of obesity with special emphasis on the latest research manifolds.

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Introduction

Obesity can be described as the "New World Syndrome". Its prevalence is on continuous rise in all age groups of many of the developed countries in the world. Statistical data reveals that the problem of obesity has increased from 12–20% in men and from 16–25% in women over the last ten years [ 1 ]. Recent studies suggest that nearly 15–20% of the middle aged European population are obese [ 2 ] and that in USA alone it is responsible for as many as 3,00,000 premature deaths each year [ 3 ]. Obese patients have been associated with increased risk of morbidity and mortality relative to those with ideal body weight [ 4 ]. Even modest weight reduction in the range of 5–10% of the initial body weight is associated with significant improvements in a wide range of co-morbid conditions [ 5 – 9 ]. Obesity, which was once viewed as the result of lack of will power, or a lifestyle "choice" – the choice to overeat and under exercise, is now being considered more appropriately by the modern world as a chronic disease, which requires effective strategies for its management.

Obesity, in simple terms, may be defined as a state of imbalance between calories ingested versus calories expended which would lead to excessive or abnormal fat accumulation. Body Mass Index (BMI) is a measure of weight corrected for height and which reflects the total body fat and has been the most accepted parameter for defining over weight [ 10 ].

Optimal BMI increases with age. WHO also classified over weight according to BMI [ 11 ]. There is a very good correlation between BMI and the percentage of body fat in large populations.

Percent Body fat = 1.2 (BMI) + 0.23 (age) - 10.8 (gender) - 5.4

Where gender = '1' for men and '0' for women.

It follows from this equation that for a given height and weight, the percentage of body fat is about 10% higher in women compared to men. The reason for this could be that in women, the excess body fat is usually distributed as subcutaneous fat and is mainly peripheral (thighs, buttocks, breasts) where as in men there is a relative excess of body fat stored in abdominal cavity as abdominal subcutaneous fat.

New classifications of over weight may be based on cut-off points for simple anthropometric measures such as waist hip ratio, total adiposity and intra-abdominal fatness. There exists a correlation between increased BMI, mortality due to allied risks which is depicted in Fig. 1

Correlation between increased BMI and risk of mortality

Aetiology of obesity

Obesity is not a single disorder but a heterogeneous group of conditions with multiple causes each of which is ultimately expressed as obese phenotype. Obesity involves complex aetiological links between the genetic, metabolic and neural frameworks on one hand and behavior, food habits, physical activity and socio-cultural factors on the other (Table 1 ).

Genetic considerations

Although obesity had a genetic component, it is not a simple genetic disorder. There is an underlying genetic predisposition to obesity on to which environmental factors are layered. The discovery of 'ob' gene, which was mapped to chromosome 7, has led to a renewed interest in understanding the patho-biological basis of genetic predisposition in obesity. The 'ob' gene codes a hormone called leptin, a 167 amino acid protein and was supposed to be produced in white and brown adipose tissue and placenta [ 12 ]. The leptin receptors are concentrated in hypothalamus and belong to the same class of IL-2 and growth hormone receptors [ 13 ]. Any mutation of 'ob' gene leads to improper coding of leptin, which further results in obesity [ 14 ]. The effects of the 'ob' gene are mediated through effects on both energy intake and energy expenditure. Obesity can also be considered as a "complex trait" as many other genes coding proteins like apolipoprotein B, D, E, β 3 -adrenergic receptor [ 15 ], dopamine D 2 -receptor, tumor necrosis factor (TNF), glucocorticoid receptor etc. are associated with it. So far, 200 genes, gene markers and chromosomal regions have been associated with human obesity [ 16 ].

Neurobiology

Two neurotransmitters neuropeptide Y (NPY) and serotonin (5-HT) are found to play a major role in body weight regulation. NPY is a 36 amino acid peptide, which is concentrated mainly in the hypothalamus; a region crucial to regulation of appetite [ 17 ] has emerged as a possible key neurotransmitter candidate for the regulation of energy homeostasis. Increased NPY activity has been found in the hypothalamus of obese rodents [ 18 ]. NPY increases food in-take through its interaction with a unique Y5 subtype of NPY receptor and hence Y5 receptor antagonists could be effective in the treatment of obesity [ 19 ].

The inhibitory actions of 5-HT on food in-take have been localized to the hypothalamic para ventricular nucleus (PVN), the site at which NPY is most active in inducing feeding behavior [ 20 ]. 5-HT induced reduction in food in-take is mediated by post-synaptic 5-HT IB receptors. The hypophagic actions of 5-HT may be mediated at least partly through the NPY pathway. For example, 5-HT antagonist which stimulates feeding increases NPY concentrations in the arcuate and para ventricular nuclei of the hypothalamus [ 21 ]. Similarly, a 5-HT agonist, which reduces food intake significantly, reduces NPY concentrations in the hypothalamic para ventricular nucleus. Corticotrophin releasing factor (CRF) which also causes weight loss by reducing appetite and act in opposing to NPY on the regulation of energy balances. Cholecystokinin (CCK), a neurotransmitter present in the brain plays a physiological role as a meal termination (satiety) signal between the two receptors such as CCK A and CCK B , CCK acted at CCK A receptors [ 22 ]. Hence, CCK A agonist could also be useful in the treatment of obesity.

Environmental factors

These factors play a critical role in the development of obesity by unmasking genetic or metabolic susceptibilities. Environmental influences act via an increase in energy intake or a decrease in energy expenditure with little physical activity and hence there is increased likelihood of becoming obese. Sedentary behaviors, notably television watching, car ownership also contributes to the risk of obesity. The role of passive over consumption [ 23 ], eating disorders, and preference for high carbohydrate diet also play an important role in increasing the risk of obesity. Other food habits like smoking and alcohol consumption lowers body weight and results in higher BMI respectively.

Psycho-social impact

A number of individual characteristics may place individuals at increased risk of obesity. Restrained eating also plays a role in aetiology of obesity. Restrained eaters report more food carvings and binge eating [ 24 ]. One of the characteristic features of dietary restraints is the tendency towards disinhibited eating in particular circumstances. Restrained eaters may be more susceptible to the availability of highly palatable foods, which act as a stimulus for excess food consumption.

Obesity-associated diseases and risk factors

Cardiovascular diseases (cvd).

Hypertension

Coronary heart disease

Cerebrovascular disease

Varicose veins

Deep venous thrombosis

The increased risk of CVD is 2-fold in women of BMI 25–28.9 kg/m 2 and 3.6 fold for BMI in 29 kg/m 2 or more. In males a 10% increase in body weight increases risk of CVD by 38%, where as 20% weight risk corresponds with 86% increased risk. Blood pressure is increased by 6 mm systole and 4 mm diastole for a 10% gain in body fat. Hyper tension is prevalent in obese adults at a rate of 2.9 fold than non-obese population and weight reduction reduces risk of developing hyper tension [ 25 ].

Respiratory diseases

Sleep apnoea

Hypoventilation syndrome

There are a number of ways in which obesity affects lung function [ 26 ]. An increased amount of fat in the chest wall and abdomen limits respiratory excursion reducing lung volume. As the obesity worsens, so do the apnoeic episodes resulting in frequent awakening and the resultant sleep deprivation produces daytime somnolence.

Metabolic disorders

Hyperlipidemia

Diabetes mellitus

Insulin resistance

Menstrual irregularities

There is a consistent graded relationship between increased BMI and prevalence of NIDDM and insulin resistance [ 27 ]. Over 10 to 15 million Americans with type 2 diabetes are obese [ 28 ]. A mean weight loss of 7% weight reduces risk of developing type 2 diabetes by more than 55% [ 29 ]. BMI above 35 kg/m 2 increases the risk by 93 fold in women and by 42 fold in men. Obesity is associated with lipid disorders in which elevated levels of cholesterol, triglycerides, LDL-cholesterol and low levels of HDL-cholesterol are observed. For every 1 kg of weight loss, there is a corresponding reduction by about 1% in HDL and reduction by 3% of triglycerides. It has been observed that modest weight loss reduces lipid abnormalities [ 30 ] and diabetes mellitus [ 31 ].

Gastrointestinal disorders

Fatty liver and cirrhosis

Haemorrhoids

Colorectal cancer

Gall bladder disease is the most common gastrointestinal disorder in obese individuals. Obese women have a 2.7 fold increase in the prevalence of gall bladder disease. There is an increased risk of gallstones in individuals having BMI of 20 kg/m 2 or more. The mortality rates of cancer of the stomach and pancreas were higher in obese individuals.

Malignancies

Breast cancer

Endometrial Cancer

Prostrate Cancer

Cervical Cancer

Obese women have higher incidence of endometrial, ovarian, cervical and postmenopausal breast cancer, while obese men have incidents of prostrate cancer.

However, it remains to be confirmed whether these malignancies occur as a result of hormonal changes associated with obesity or due to specific dietary pattern.

Miscellaneous

Arthritis and bone mass

Stress is associated with the consumption of high fat foods and leads to weight gain. Obesity is also associated with osteoarthritis of hip and knee although in some cases, mechanical stress associated with obesity leads to osteoarthritis [ 32 ]. Obese women have a higher risk of obstetric complication and have increased risk of caesarean delivery due to variety of foetal size. Recently, an increased risk of neural tube defects especially spinabifida has been reported in women with BMI greater than 29 kg/m 2 .

Prevention of obesity

Obesity is a serious, chronic medical condition, which is associated with a wide range of debilitating and life threatening conditions. The fact that obesity prevalence continues to increase at an alarming rate in almost all regions of the world is of major concern. Hence, an effective control of obesity requires the development of coherent strategies that tackle the main issues related to preventing:

i) The development of over weight in normal weight individuals

ii) The progression of over weight to obesity in those who are already over weight

iii) Weight regain in those who have been over weight or obese in the past but who have since lost weight and

iv) Further worsening of a condition already established.

The prevention of obesity involves action at several levels i) Primary ii) Secondary iii) Tertiary [ 33 ]. Objective of primary prevention is to decrease the number of new cases, secondary prevention is to lower the rate of established cases in the community and tertiary prevention is to stabilize or reduce the amount of disability associated with the disorder. When the attention is focused on the multi-factorial condition such as coronary heart disease (CHD), primary prevention of this involves national programmes to control blood cholesterol levels and secondary prevention deals with reducing CHD risk in those with existing elevated blood cholesterol levels while tertiary action would be associated with preventing re-infarction in those who had a previous heart attack. However, this classification system for prevention of obesity results in a great deal of ambiguity and confusion. To avoid this, the US institute of medicine [ 34 ] has proposed alternative classification of system. The new system separates prevention efforts into 3 levels. Universal (or) public health measures (directed at every one in the population), selective (for a sub-group who may have an above average risk of developing obesity) and indicated (targeted at high risk individuals who may have a detectable amount of excess weight which fore-shadows obesity). However, preventive measures for any disorder may not be helpful in all cases hence, proper management strategies can be integrated along with prevention programmes.

Management of obesity

Management include both weight control or reducing excess body weight and maintaining that weight loss, as well as, initiating other measures to control associated risk factors. Periodic evaluation for obesity should be done by the measurement of BMI, measurement of waist circumference etc., to assess risk factors. Based on the evaluation, appropriate treatment can be suggested. Treatment may consist of modification of diet, increased physical activity, behavioral therapy, and in certain circumstances weight loss medication and surgery.

Dietary therapy

Restrictions of calories represent the first line therapy in all cases except in cases with pregnancy, lactation, terminal illness, anorexia nervosa, cholelithiasis and osteoporosis. Low calorie diets (LCD), which provide 100–1500 kcal/day, resulted in weight loss of 8% of baseline body weight over six months but on long run most of the lost weight is regained [ 35 ].

Very low calories diets (VLCD), which provide 300–800 kcal/day, can be useful in severely obese patients under strict medical supervision. They are found to produce 13% weight loss over six months, i.e. they produce greater initial weight loss than LCDs, however, the long-term (>1 year) weight loss by VLCD's is not found superior to that of the LCDs.

Meal replacement programmes and formula diets can be used as an effective tool in weight management [ 36 ]. Optifast, Medifast are available through physians or hospitals as part of packaged weight-reduction programmes. These products appear to be safe, but maintenance of weight loss over the long term is difficult.

Other over the counter (OTC) variations to formula diets includes Slimfast and Ultra slimfast. The consumer is instructed to drink the formulations and use it to replace one or two meals.

Fat substitutes like Olestra (Olean), which is a non-digestible, non-caloric fat, can be used in food preparations taken by obese patients.

It has been observed that calorie restriction alone has remarkable effects compared to exercise alone [ 37 – 39 ]. A loss of 5% initial weight achieved with diet and exercise is associated with significant improvement in glycylated haemoglobin A IC and that diet control can be useful to treat co morbidities of obesity such as diabetes [ 40 ].

Physical activity

All individuals can benefit from regular exercise [ 41 ]. Physical activity, which increases energy expenditure, has a positive role in reducing fat storage and adjusting energy balance in obese patients. Various exercises preceded and followed by short warm up and cool down sessions help to decrease abdominal fat, prevent loss of muscle mass. Studies revealed that patients who exercise regularly had increased cardio vascular fitness [ 42 , 43 ] along with betterment in their mental and emotional status. Hence a minimum of 30 minutes exercise is recommended for people of all ages [ 44 ] as part of comprehensive weight loss therapy.

Behaviour therapy

Behaviour therapy is a useful adjunct when incorporated into treatment for weight loss and weight maintenance. Patients need to be trained in gaining self-control of their eating habits. Behaviour modification programmes which seek to eliminate improper eating behaviours (eating while watching TV, eating too rapidly, eating when not hungry etc.,) include individual or group counseling of patients.

Self-help groups (weight watchers, Nutri-System) use a program of diet, education and self-monitoring like maintenance of logbook, keeping an account of food intake etc are beneficial.

Pharmacotherapy

Drug treatment is advised only for subjects with BMI > 27 and with associated risk factors or with a BMI > 30 [ 45 ] and thus at medical risk because of their obesity. It should not be used for "cosmetic" weight loss. Weight loss medications should be used only as an adjunct to dietary and exercise regimes coupled with a program of behavioural treatment and nutritional counseling.

Pharmacological approaches in obesity treatment

Most available weight loss medications are "appetite–suppressant" medications. The initial drugs used for appetite suppression were amphetamine [ 46 ], metamphetamine and phenmetrazine (Preludin) and are no longer used in treatment of obesity because of their high potential for abuse.

Inhibitors of 5-hyroxytryptamine (5-HT) reuptake, fenfluramine and dexfenfluramine were licensed for obesity but proved to cause pulmonary hyper tension and increased valvular heart disease [ 47 ] and have been withdrawn from the market. Drugs like phendimetrazine (Plegine), diethylpropion (Tenuate), phentermine (Lonamin) etc., are being marketed but have been classified as controlled substances and are recommended for short-term use only.

The newest agents available for weight loss are sibutramine (Meredia) and orlistat (Xenical). They are the only weight loss medications approved by the US Food and Drug Administration (FDA) for long-term use [ 48 ] in significantly obese patients, although their safety and effectiveness have not been established for use beyond one year.

Sibutramine is the serotonin and norepinephrine re-uptake inhibitor, which induces decreased food intake and increased thermogensis [ 49 – 52 ]. In clinical trials, sibutramine showed a statistical improvement in amount of weight lost versus placebo [ 53 ]. It limits decline of metabolic rate that typically accompanies weight loss [ 54 ]. However, this agent is contraindicated in-patient with known seizure disorders, high blood pressure, congestive heart failure (CHF) a history of myocardial infraction and arrhythmias.

Orlistat is a potent and irreversible inhibitor of gastric, pancreatic lipases. It blocks the digestion of approximately 30% of the ingested dietary triglycerides. Studies proved that it produces 5% more weight loss than in control groups [ 55 ]. It is now available on prescription as Xenical ® (Orlistat-120 mg). The most commonly reported side effects include oily stools, soft stool [ 56 ], and increased defecation and decreased absorption of fat-soluble vitamins (A, D, E and K). Hence, patient may be recommended intake of fat-soluble vitamins [ 57 ] along with it. When used in conjugation with diet it was found to improve glycemic control and cardiovascular disorders [ 58 , 59 ].

In general, monotherapy in obese patients produced sub-optimal weight loss [ 60 ] but the use of more than one weight loss medication at a time (combined drug therapy) is not approved [ 61 ] and hence such an off-label use of combinations of drugs for weight loss is not recommended except as part of a research study.

Drugs under development

There has been a wide search for effective drugs for the treatment of obesity. Some of the promising drug development research areas are mentioned below.

Amylin is a peptide secreted with insulin in response to food intake that shares many other properties with established adiposity signals like insulin and leptin. Its circulating levels can be correlated with body fat. Preclinical studies have shown that amylin complements the effects of insulin in mealtime glucose regulation via several effects, which include a suppression of post meal glucagon secretion, a decrease in gastric emptying, and a decrease in food intake [ 62 ]. The drug pramlintide, a synthetic analogue of amylin is currently in phase III trials.

11β-hydroxysteroid dehydrogenase type-1 (11β-HSD-1) is an enzyme that increases cortisol levels in adipocytes. Studies on mice lacking gene for 11β-HSD-1 suggest that they are resistant to diet induced obesity [ 63 ]. An 11β-HSD-1 inhibitor being developed by Biovitrum is currently in clinical testing.

Stimulation of β 3 adrenoreceptors (β 3 -ARs) by selective agonists improves insulin action and stimulates energy metabolism. In animals, chronic β 3 -AR agonist treatment causes body weight reduction, which is almost entirely due to decrease in body fat [ 64 ]. At least a dozen pharmaceutical companies are in the process of developing β 3 -AR drugs, some of which are already in human testing. AD9677 a β-adrenoceptor agonist is in phase II trails.

The botanical P57 is an extract of steroidal glycosides derived from South African Cactus . The potent appetite suppression may occur via the melanocortin-4 (MCR-4) saponins from the Platycodi radix and Salacia reticulata have been shown to inhibit pancreatic lipase, producing weight loss and reduction of fatty liver in laboratory animals [ 65 ]. Currently, P57 is in Phase II testing and Table 2 summarizes some other important drugs union are under clinical trials for the treatment of obesity.

Apart from drug treatment, surgery is also indicated when BMI is exceedingly high (>40 kg/m 2 or >30 kg/m 2 with obesity-related medical co-morbidities) and when other treatment modalities have failed [ 66 ]. The most popular surgical procedures used for treatment of severe obesities involve gastric portioning or gastroplasty and gastric by-pass. The gastroplasty procedures create a small gastric pouch, which is drained through a narrow calibrated stoma [ 67 , 68 ]. The intake of solids is therefore considerably limited. Gastric by-pass surgery creates a larger pouch emptied by an anastomosis directly into the jejunum, bypassing the duodenum. It is considered now as the most effective and safe surgery for morbid obesity [ 69 , 70 ]. This technique induces weight loss by combining restricted intake and a moderate degree of malabsorbtion [ 71 ]. Initial loss of weight is greater after this procedure than following gastroplasty [ 72 ].

Gastric and nutritional complications [ 73 ] may be serious implications of the surgery. Nutritional deficiencies and intractable vomiting are frequently associated with surgery. Surgical treatments for obesity resolve most co-morbidities of severe obesity such as hypertension [ 74 , 75 ], serum lipid levels [ 76 ] and diabetes mellitus [ 77 , 78 ].

Obesity is not a social condition but is a rampant disease. Obesity cannot be overviewed as just a matter of overeating and lack of will power but must be considered as a major genetic aetiology modified by environment and should be treated vigorously in the same manner that we now apply to other diseases. A better understanding of the aetiological determinants in individual subjects will provide a basis for more rational intervention to prevent this recalcitrant public health problem. With the increasing awareness and ongoing research in this area there is a considerable reason for optimism that the next coming years will bring better treatment for the obese.

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Nammi, S., Koka, S., Chinnala, K.M. et al. Obesity: An overview on its current perspectives and treatment options. Nutr J 3 , 3 (2004). https://doi.org/10.1186/1475-2891-3-3

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Received : 28 February 2004

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394 Obesity Essay Topics & Research Questions + Examples

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• The Causes and Effects of Obesity
• Childhood Obesity: Causes and Effects
• Childhood Obesity: Causes and Solutions
• Childhood Obesity: The Parents’ Responsibility
• Depression as It Relates to Obesity
• Junk Food and Children’s Obesity
• Health Promotion Proposal Obesity Prevention
• Obesity Prevention and Weight Management Theory
• Obesity as a Disease: Arguments For and Against
• Parents Are Not to Blame for Obesity in Children
• Obesity: A Personal Problem and a Social Issue Obesity is a problem affecting many persons and society as a whole. According to World Health Organization, over 40% of the US population is either overweight or outright obese.
• Obesity From Sociological Perspectives The social problem under focus is obesity originating from Latino food norms. The problem of obesity is the direct result of adherence to social norms.
• Health Promotion for Obesity in Adults This is a health promotion proposal for preventing obesity among adults in the US. People get obesity when they acquire a given body mass index.
• Unhealthy Food Culture and Obesity Unhealthy food culture plays a significant role in developing health-related diseases, including its contribution to obesity.
• Obesity Issue: Application of Nursing Theory This analysis will show that well-established theories are valuable to nursing problem-solving as frameworks for analyzing issues and planning solutions.
• Obesity Prevention: Social Media Campaign A variety of programs aimed at reducing the risk of obesity has been suggested by healthcare practitioners and scholars. Among them, diet interventions are highly popular.
• Link Between Obesity and Genetics Obesity affects the lives through limitations implemented on the physical activity, associated disorders, and even emotional pressure.
• Childhood Obesity: Causes and Effects Childhood obesity has many causes and effects, which denotes that parents and teachers should make children with obesity engage in regular physical exercise in school and at home.
• Childhood Obesity Study and Health Belief Model A field experiment will be used in the research to identify the impact of a healthy lifestyle intervention on children diagnosed with obesity.
• Health Promotion Strategies for Obesity The paper outlines and critically analyses the population based strategy as a method of managing and preventing obesity used in United Kingdom.
• Childhood Obesity: Prevention and Mitigation Over the past three decades, childhood obesity has developed into an epidemic and is considered as one of the major health issues in the world.
• How to Reduce Obesity and Maintain Health? Health is becoming a matter of grave concern, especially the health of teenagers and adolescents, who are becoming increasingly overweight and obese.
• Link Between Watching Television and Obesity One of the primary causes of obesity is a sedentary lifestyle, which often includes excessive screen-watching periods.
• Obesity in Children and Adolescents: Quantitative Methods Obesity in children and adolescents has increasingly become prevalent in the recent past and is now a major problem in most developed countries.
• Children Obesity Prevention Proposals The purpose of this paper is to propose the study of motivational interviewing benefits in preventing childhood obesity in the context of the literature review method.
• Children Obesity Research Method and Sampling This paper presents a research method and sampling on the investigation of the issue of childhood obesity and the impact parents` education might have on reducing excess weight.
• Obesity: Causes, Consequences, and Care Nowadays, an increasing number of people suffer from having excess weight. This paper analyzes the relationship between obesity and other diseases.
• Obesity Management and Intervention Many patients within the age brackets of 5-9 admitted in hospital with obesity cases have a secondary diagnosis of cardiovascular disease exceptionally high blood pressure.
• Childhood Obesity: Problem Analysis The introduced project addresses childhood obesity problem and highlights the inconsistency between the existing programs and their implementation in real life.
• Childhood Obesity: Quantitative Annotated Bibliography Childhood obesity is a problem that stands especially acute today, in the era of consumerism. Children now have immense access to the Internet.
• Childhood Obesity and Nutrition The prevalence of childhood obesity in schools can be compared to an epidemic of a virulent disease on a global scale.
• Obesity Management: Hypothesis Test Study This paper will show how a hypothesis test study can help inform evidence-based practice regarding obesity management.
• Childhood Obesity Prevention: The Role of Nursing Education Nurse practitioners have to deal with childhood obesity challenges and identity healthy physical and environmental factors to help pediatric patients and their parents.
• Prevention of Obesity in Children The aim of the study is to find out whether the education of parent on a healthy lifestyle for the children compared with medication treatment, increase the outcome and prevention of obesity.
• Nutrition: Fighting the Childhood Obesity Epidemic Childhood obesity is defined variably as the condition of excessive body fat in children that adversely his/her health. It has been cited as a serious health concern issue in many countries.
• Obesity as a Global Health Issue The purpose of this research is to identify obesity as a global health issue, evaluate the methods and findings conducted on obesity, and find solutions to reduce obesity globally.
• Obesity: Background and Preventative Measures Obesity is an epidemic. It tends to have more negative than positive effects on the economy and can greatly reduce one’s life expectancy.
• Pediatric Obesity and Self-Care Nursing Theory The presence of excess body fat in children has to be given special consideration since healthy childhood is a prerequisite to normal physical and psychological maturation.
• Obesity in School-Aged Children as a Social Burden In addition to personal concerns, overweight and obese children are at risk for long-term health consequences, including cardiovascular problems and additional comorbidities.
• Childhood Obesity Causes: Junk Food and Video Games The problem of “competitive foods and beverages” that are sold in schools outside the existing breakfast and lunch programs has been discussed for a while now.
• Obesity as American Social Health Issue In the public health sector, obesity is defined as a social problem because it is associated with the eating habits and bodily lifestyles of every community.
• Childhood Obesity and Public Policies in England The study identifies the preventive measures of the English government to deal with childhood obesity and compares the trends in England with the rest of the UK.
• Obesity and Iron Deficiency Among College Students The study seeks to establish the relationship between obesity and iron deficiency by analyzing the serum hepcidin concentration among individuals aged between 19 to 29 years.
• Childhood Obesity and Overweight Issues The paper discusses childhood obesity. It has been shown to have a negative influence on both physical health and mental well-being.
• Obesity in the World: the Prevalence, Its Effects to Human Health, and Causes There are various causes of obesity ranging from the quantity of food ingested to the last of physical exercises that utilize the accumulated energy.
• Childhood Obesity: Methods and Data Collection The first instrument that will be used in data collection is body mass index (BMI). The BMI is measured by dividing a patient’s weight in kilograms by height in meters squared.
• Childhood Obesity Prevention: Physical Education and Nutrition The paper examines how physical education in schools can prevent child obesity and how to educate parents about the importance of proper nutrition.
• Obesity Counteractions in Clark County, Washington The prevalence of obesity has been increasing sharply among children and adults in the Clark County because of the failure to observe healthy eating habits.
• Betty Neuman’s System Model for Adult Obesity Betty Neuman’s system model can beneficially influence a physical and emotional state of the person who is experiencing difficulties with being overweight.
• Childhood Obesity and Health Promotion Today, childhood obesity is one of the critical health concerns. Being an important factor impacting the future of the nation, children`s health should be cultivated.
• Food Ads Ban for Childhood Obesity Prevention In order to prevent childhood obesity, it is necessary to ban food ads because they have adverse effects on children’s food preferences, consumption, and purchasing behaviors.
• Technological Progress as the Cause of Obesity Obesity is the increase of the body’s weight over the natural limit because of accumulated fats. Technology is a cost to the lost creativity and control over the required healthy lifestyle.
• Obesity in Adolescence as a Social Problem The paper states that adolescence is one of the most crucial developmental phases of human life during which the issue of obesity must be solved.
• Discussion of Freedman’s Article “How Junk Food Can End Obesity” David Freedman, in article “How Junk Food Can End Obesity”, talks about various misconceptions regarding healthy food that are common in society.
• Eating Fast Food and Obesity Correlation Analysis The proposed study will attempt to answer the question of what is the relationship between eating fast food and obesity, using correlation analysis.
• The Effects of Gender on Child Obesity The high percentage of women’s obesity prevalence is a result of poor nutrition in childhood and access to greater resources in adulthood.
• Prevention of Obesity in Teenagers This paper aims to create an education plan for teenage patients and their parents to effectively inform them and help them avoid obesity.
• Depression and Other Antecedents of Obesity Defeating the inertia about taking up a regular programme of sports and exercise can be a challenging goal. Hence, more advocacy campaigns focus on doing something about obesity with a more prudent diet.
• Obesity Prevention in Community: Strategic Plan This paper is a plan of how to change the way the community should treat obesity and improve people’s health through the required number of interventions.
• Childhood Obesity Study: Literature Review Obesity in children remains a major public health issue. A growing body of evidence suggests that social networks present a viable way to improve the situation.
• Childhood Obesity and Self-Care Deficit Theory To help the target audience develop an understanding of the effects that their eating behavior has on their health, Dorothea Orem’s Theory of Self-Care Deficit can be utilized.
• Childhood Obesity: Data Management The use of electronic health records (EHR) is regarded as one of the effective ways to treat obesity in the population.
• Childhood Obesity Problem Solution As a means of solving the problem of childhood obesity, the author of the research proposes to develop healthy custom menus for schools under a program called “Soul Food.”
• Treat and Reduce Obesity Act and Its Potential The paper discusses the background, processing, and potential consequences of a Congress bill presented as H.R.1953: Treat and Reduce Obesity Act of 2017.
• Childhood Obesity, Social Actions and Intervention This literature review presents the major social actions and family-based interventions that have been in use to address the problem of obesity in children.
• Humanistic Theory in Childhood Obesity Research The humanistic theory will assist in research investigating how the use of dieting and parental supervision can help to resolve the issue of obesity.
• Nutrition: Obesity Pandemic and Genetic Code The environment in which we access the food we consume has changed. Unhealthy foods are cheaper, and there is no motivation to eat healthily.
• Obesity From Sociological Imagination Viewpoint Most obese individuals understand that the modern market is not ready to accept them due to negative sociological imagination.
• Childhood Obesity as an International Problem This paper explores the significance of using the web-based technological approach in combating obesity among Jewish children.
• Obesity in People with Intellectual Disabilities’: The Article Review Mashall, McConkey, and Moore, in the ‘Obesity in People with Intellectual Disabilities’ article, seek to assess obese and overweight individuals.
• Janet Tomiyama’s “Stress and Obesity” Summary “Stress and Obesity,” an article by A. Janet Tomiyama, covers the interrelation between the two issues listed in the title and their mutual influence in psychological terms.
• Obesity Problem in the United States Obesity is not just people going fat; it is a disease that causes maladies like type-2 diabetes, heart disease, cancer and strokes.
• Personal Issues: Marriage, Obesity, and Alcohol Abuse The actions of every person have a particular impact on society and its development, and this impact is sometimes underestimated.
• Childhood Obesity and Parent Education: Ethical Issues The proposed research reveals important insights about obesity among children and infants. Apart from the positive intentions of the research, different ethical considerations have to be described.
• Diet and Lifestyle vs Surgery in Obesity Treatment The research would assess the effectiveness of dietary interventions and lifestyle changes in comparison with the bariatric surgery to determine the methods’ advantages.
• Childhood Obesity and Healthy Lifestyle Education The research hypothesis is if parents are educated about a healthy lifestyle, then positive outcomes and prevention of childhood obesity will increase.
• Obesity Education Plan for Older Adults The given paper presents an obesity education plan targeted at adults and older adults who are overweight or obese and, therefore, are at risk of developing various diseases.
• Diabetic Patients with Obesity or Overweight The proposed study was expected to provide the knowledge helping to improve the quality of care and outcomes for diabetic patients with obesity or overweight.
• Aspects of Obesity Risk Factors Obesity is one of the most pressing concerns in recent years. Most studies attribute the rising cases of obesity to economic development.
• Obesity: High Accumulation of Adipose Tissue It is important to point out that obesity is a complex and intricate disease that is associated with a host of different metabolic illnesses.
• Should fast-food restaurants be liable for increasing obesity rates?
• Does public education on healthy eating reduce obesity prevalence?
• Is obesity a result of personal choices or socioeconomic circumstances?
• Should the government impose taxes on soda and junk food?
• Weight loss surgery for obesity: pros and cons.
• Should restaurants be required to display the caloric content of every menu item?
• Genetics and the environment: which is a more significant contributor to obesity?
• Should parents be held accountable for their children’s obesity?
• Does weight stigmatization affect obesity treatment outcomes?
• Does the fashion industry contribute to obesity among women?
• Childhood Obesity During the COVID-19 Pandemic While the COVID-19 pandemic elicited one of the worst prevalences of childhood obesity, determining its extent was a problem due to the lockdown.
• Overweight and Obesity Prevalence in the US Obesity is a significant public health problem recognized as one of the leading causes of mortality in the United States. Obesity and overweight are two common disorders.
• Obesity Screening Training Using the 5AS Framework The paper aims to decrease obesity levels at the community level. It provides the PCPs with the tools that would allow them to identify patients.
• Prevalence and Control of Obesity in Texas Obesity has been a severe health issue in the United States and globally. A person is obese if their size is more significant than the average weight.
• Preventing Obesity Health Issues From Childhood The selected problem is childhood obesity, the rates of which increase nationwide yearly and require the attention of the government, society, and parents.
• Describing the Problem of Childhood Obesity Childhood obesity is a problem that affects many children. If individuals experience a health issue in their childhood, it is going to lead to negative consequences.
• Researching of Obesity in Florida It is important to note that Florida does not elicit the only state with an obesity problem, as the nation’s obesity prevalence stood at 42.4% in 2018.
• Preventing Obesity Health Issues From the Childhood The paper is valuable for parents of children who are subject to gaining excess weight because the report offers how to solve the issue.
• The Social Problem of Obesity in Adolescence The social worker should be the bridge uniting obese individuals and society advertising social changes, and ending injustice and discrimination.
• Obesity and Health Outcomes in COVID-19 Patients The COVID-19 pandemic has posed many challenges over the last three years, and significant research has been done regarding its health effects and factors.
• Childhood Obesity in the US from Economic Perspective The economic explanation for the problem of childhood obesity refers to the inability of a part of the population to provide themselves and their children with healthy food.
• Obesity in the United States of America The article discusses the causes of the obesity pandemic in the United States of America, which has been recognized as a pandemic due to its scope, and high prevalence.
• The Problem of Childhood Obesity Obesity in childhood is a great concern of current medicine as the habits of healthy eating and lifestyle are taught by parents at an early age.
• Oral Health and Obesity Among Adolescents This research paper developed the idea of using dental offices as the primary gateway to detect potential obesity among Texas adolescents.
• Obesity, Weight Loss Programs and Nutrition The article addresses issues that can help increase access to information related to the provision of weight loss programs and nutrition.
• Childhood Obesity in the US From an Economic Perspective Looking at the problem of childhood obesity from an economic point of view offers an understanding of a wider range of causes and the definition of government intervention.
• Diet, Physical Activity, Obesity and Related Cancer Risk The paper addresses the connection between cancer and physical activity, diet, and obesity in Latin America and the USA. The transitions in dietary practices may be observed.
• The Current Problem of Obesity in the United States The paper raises the current problem of obesity in the United States and informs people about the issue, as well as what effect obesity can have on health.
• Childhood and Adolescent Obesity and Its Reasons Various socio-economic, health-related, biological, and behavioral factors may cause childhood obesity. They include an unhealthy diet and insufficient physical activity and sleep.
• Pediatric Obesity and Its Treatment Pediatric obesity is often the result of unhealthy nutrition and the lack of control from parents but not of health issues or hormonal imbalance.
• Impact of Obesity on Healthcare System Patients suffering from obesity suffer immensely from stigma during the process of care due to avoidance which ultimately affects the quality of care.
• Trending Diets to Curb Obesity There are many trending diets that have significant effects on shedding pounds; however, the discourse will focus on the Mediterranean diet.
• Issues of Obesity and Food Addiction Obesity and food addiction have become widespread and significant problems in modern society, both health-related and social.
• Diet, Physical Activity, Obesity, and Related Cancer Risk One’s health is affected by their lifestyle, which should be well managed since childhood to set a basis for a healthier adulthood.
• Articles About Childhood Obesity The most straightforward technique to diagnose childhood obesity is to measure the child’s weight and height and compare them to conventional height and weight charts.
• Obesity Prevention Policy Making in Texas Obesity is a national health problem, especially in Texas; therefore, the state immediately needed to launch a policy to combat and prevent obesity in the population.
• Obesity and How It Can Cause Chronic Diseases Obesity is associated with increased cardiovascular diseases, and cancer risks. The modifications in nutrition patterns and physical activity are effective methods to manage them.
• Physical Wellness to Prevent Obesity Heart Diseases Heart disease remains to be one of the most severe health concerns around the world. One of the leading causes of the condition is obesity.
• Obesity and General State of Public Health Obesity is a condition caused by an abnormal or excessive buildup of fat that poses a health concern. It raises the risk of developing various diseases and health issues.
• Ways of Obesity Interventions The paper discusses ways of obesity interventions. It includes diet and exercise, patient education, adherence to medication, and social justice.
• Obesity, Cardiovascular and Inflammatory Condition Under Hormones The essay discusses heart-related diseases and obesity conditions in the human body. The essay also explains the ghrelin hormone and how it affects the cardiovascular system.
• Obesity in Adolescence in the Hispanic Community The health risks linked to Hispanic community adolescent obesity range from diabetes, heart problems, sleep disorders, asthma, and joint pain.
• Obesity as a Wellness Concern in the Nursing Field A critical analysis of wellness can provide an understanding of why people make specific health-related choices.
• Physio- and Psychological Causes of Obesity The paper states that obesity is a complex problem in the formation of which many physiological and psychological factors are involved.
• How Junk Diets Can Reduce Obesity To control obesity there is a need to ensure that the junk foods produced are safe for consumption before being released into the foods market.
• The Problem of Obesity: Weight Management Obesity is now a significant public health issue around the world. The type 2 diabetes, cardiac conditions, stroke, and metabolism are the main risk factors.
• Behavioral Modifications for Patients With Obesity This paper aims to find out in obese patients, do lifestyle and behavioral changes, compared to weight loss surgery, improve patients’ health and reduce complications.
• Sleep Deprivation Effects on Adolescents Who Suffer From Obesity The academic literature on sleep deprivation argues that it has a number of adverse health effects on children and adolescents, with obesity being one of them.
• Hypertensive Patients Will Maintain Healthy Blood Pressure and Prevent Obesity Despite hypertension and obesity are being major life threats, there are safer lifeways that one can use to combat the problem.
• The Consequences of Obesity: An Annotated Bibliography To review the literature data, the authors searched for corresponding articles on the PubMed database using specific keywords.
• The link between excess weight and chronic diseases.
• The role of genetics in obesity.
• The impact on income and education on obesity risks.
• The influence of food advertising on consumer choices.
• Debunking the myths related to weight loss.
• Obesity during pregnancy: risks and complications.
• Cultural influences on eating patterns and obesity prevalence.
• Community initiatives for obesity prevention.
• The healthcare and societal costs of obesity.
• The bidirectional relationship between sleep disorders and obesity.
• Evolving Societal Norms of Obesity The primary individual factors that lead to overeating include limited self-control, peer pressure, and automatic functioning.
• Obesity: Racial and Ethnicity Disparities in West Virginia Numerous social, economic, and environmental factors contribute to racial disparities in obesity. The rates of obesity vary depending on race and ethnicity in West Virginia.
• The Worldwide Health Problem: Obesity in Children The paper touch upon the main causes of obesity, its spread throughout the world, the major effects of the condition and ways of prevention.
• Mental Stability and Obesity Interrelation The study aims to conduct an integrative review synthesizing and interpreting existing research results on the interrelation between mental stability and obesity.
• Crutcho Public School: Obesity in School Children Numerous school children at Crutcho Public elementary school, Oklahoma City, are obese revealing how obesity is a threat to that community.
• A National Childhood Obesity Prevention Program We Can!® A national childhood obesity prevention program We Can!® explains the rules for eating right and getting active. The program also pays attention to reducing screen time.
• Obesity in Low-Income Community: Diet and Physical Activity The research evaluates the relationship between family earnings and physical activity and overweight rates of children in 8 different communities divided by race or ethnicity.
• Dealing with Obesity as a Societal Concern This essay shall discuss the health issue of obesity, a social health problem that is, unfortunately, growing at a rapid rate.
• Adolescent Obesity in the United States The article reflects the problem of overweight in the use, a consideration which the authors blame on influential factors such as age and body mass index.
• Obesity Problem Solved by Proper Nutrition and Exercise Most people who suffer from obesity are often discouraged to pursue nutrition and exercise because their bodies cannot achieve a particular look.
• Girls with Obesity: Hospital-Based Intervention This paper includes a brief description of a hospital-based intervention targeting middle-school girls with obesity.
• Hispanic Obesity in the Context of Cultural Empowerment This paper identifies negative factors directly causing obesity within the Hispanic people while distinguishing positive effects upon which potential interventions should be based.
• Health Psychology and Activists’ Views on Obesity This paper examines obesity from the psychological and activists’ perspectives while highlighting some of the steps to be taken in the prevention and curbing of the disease.
• Childhood Obesity Teaching Experience and Observations The proposed teaching plan aimed at introducing the importance of healthy eating habits to children between the ages of 6 and 11.
• Nature vs. Nurture: Child Obesity On the basis of the given assessment, it is evident that a child’s environment is a stronger influencer than his or her genetic makeup
• Care Plan: Quincy Town, Massachusetts With Childhood Obesity This study will develop a community assessment program based on the city with the aim of creating a care plan for tackling the issue of child obesity in the town.
• Exercise for Obesity Description There are numerous methods by which obesity can be controlled and one of the most effective ways is through exercising.
• Obesity and Disparity in African American Women Several studies indicate that the rate of developing obesity is the highest in African American populations in the US.
• Factors Increasing the Risk of Obesity The consumption of fast food or processed products is one of the major factors increasing the risk of obesity and associated health outcomes.
• Obesity, Diabetes and Self-Care The paper discusses being overweight or obese is a high-risk factor for diabetes mellitus and self-care among middle-aged diabetics is a function of education and income.
• Childhood Obesity in Modern Schools Most schools have poor canteens with untrained staff and poor equipment for workers. That’s why they can’t cook quality food and offer better services to students.
• Obesity in Hispanic American Citizens The issue of obesity anong Hispanic Americans occurs as a result of poor dieting choices caused by misinformed perceptions of proper eating.
• Effectiveness of a Diet and Physical Activity on the Prevention of Obesity Research indicates that obesity is the global epidemic of the 21st century, especially due to its prevalent growth and health implications.
• Community Obesity and Diabetes: Mississippi Focus Study The paper provides a detailed discussion of the correct method to be used in the state of Mississippi to control and avoid obesity and diabetes issues.
• Multicausality: Reserpine, Breast Cancer, and Obesity All the factors are not significant in the context of the liability to breast cancer development, though their minor influence is undeniable.
• The Home Food Environment and Obesity-Promoting Eating Behaviours Campbell, Crawford, Salmon, Carver, Garnett, and Baur conducted a study to determine the associations between the home food environment and obesity.
• The Problem of Childhood Obesity in the United States Childhood obesity is one of the reasons for the development of chronic diseases. In the US the problem is quite burning as the percentage of obese children increased significantly.
• Children Obesity in the United States Together with other problems and illnesses, obesity stands as one of the main difficulties in modern societies.
• The Situation of Obesity in Children in the U.S. The paper will discuss the situation of obesity in Children in the U.S. while giving the associated outcomes and consequences.
• Childhood Obesity and Healthy Lifestyles The purpose of this paper is to discuss childhood obesity and the various ways of fostering good eating habits and healthy lifestyles.
• Screen Time and Pediatric Obesity Among School-Aged Children Increased screen time raises the likelihood of children becoming overweight/obese because of the deficiency of physical exercise and the consumption of high-calorie foods.
• Policymaker Visit About the Childhood Obesity Problem The policy issue of childhood obesity continues to be burning in American society. It causes a variety of concurrent problems including mental disorders.
• Public Health Interventions and Economics: Obesity The purpose of this article is to consider the economic feasibility of public health interventions to prevent the emergence of the problem of obesity.
• Obesity Overview and Ways to Improve Health The main focus of this paper is to analyze the problems of vice marketing and some unhealthy products to teens and children.
• Nursing: Issue of Obesity, Impact of Food Obesity is a pandemic problem in America. The fast food industry is under pressure from critics about the Americans weight gain problem.
• Childhood Overweight and Obesity Childhood overweight and obesity have increased in the US. Effective transportation systems and planning decisions could eliminate such overweight-related challenges.
• Obesity Negative Influence on Public Health In recent years the increased attention has been paid to the growing obesity trends in connection to a possible negative influence on public health.
• Problematic of Obesity in Mexican Americans With this strategy, patients and guardians will embrace the best habits and eventually address the problem of obesity among Mexican Americans.
• Child Obesity Problem in the United States Obesity is a disease commonly associated with children in most countries in the world. Obesity means weighing much more than is healthy for someone.
• Obesity Rates and Global Economy The process of obesity in modern society is undoubtedly a severe obstacle to the development of the global economy, as well as to the achievement of its sustainability.
• Screen Time and Pediatric Obesity in School-Aged Children
• Obesity: Cause and Treatment
• Obesity Treatment – More Than Food
• Effects of Exercise on Obesity Reduction in Adults
• The Problem of Obesity in the Latin Community
• Obesity Prevention in Ramsey County, Minnesota
• Childhood Obesity and Its Potential Prevention
• Non-Surgical Reduction of Obesity and Overweight in Young Adults
• Obesity Prevention Due to Education
• Physical Activity and Obesity in Children by Hills et al.
• The Best Way to Address Obesity in the United States
• Nursing Diabetes and Obesity Patients
• Obesity Problem Description and Analysis
• The Issues with Obesity of Children and Adolescents
• Non-Surgical Reduction of Obesity in Young Adults
• Obesity in Children in the United States
• Childhood Obesity in Ocean Springs Mississippi
• The Problem of Children Obesity
• “Physical Activity and Obesity in Children” by A. P. Hills
• “Physical Activity and Obesity in Children” by Hills
• The Current State of Obesity in Children Issue
• Effects of Obesity on Human Lifespan Development
• Obesity and High Blood Pressure as Health Issues
• Adult Obesity: Treatment Program
• Obesity in Children and Their Physical Activity
• The Prevention of Childhood Obesity in Children of 1 to 10 Years of Age
• Obesity as a Major Health Concern in the United States
• Screen Time and Pediatric Obesity
• Technology as the Cause of Obesity
• A Dissemination Plan on Adolescent Obesity and Falls in Elderly Population
• The Issue of Obesity: Reasons and Consequences
• “Obesity and the Growing Brain” by Stacy Lu
• Obesity Disease: Symptoms and Causes
• Obesity Among Mexican-American School-Age Children in the US
• Obesity as a One of the Major Health Concerns
• Obesity: Diet Management in Adult Patients
• Children’s Obesity in the Hispanic Population
• Prevention of Childhood Obesity
• Assessing Inputs and Outputs of a Summer Obesity Prevention Program
• Designing a Program to Address Obesity in Florida
• Widespread Obesity in Low-Income Societies
• Health Policy: Obesity in Children
• Youth Obesity In Clark County in Vancouver Washington
• Obesity in Clark County and Health Policy Proposal
• Obesity: Is It a Disease?
• Clark County Obesity Problem
• Obesity Action Coalition Website Promoting Health
• Childhood Obesity: Medical Complications and Social Problems
• How to Address Obesity in the United States
• The Epidemic of Obesity: Issue Analysis
• Eating Healthy and Its Link to Obesity
• Child Obesity in North America
• Obesity in Children: Relevance of School-Based BMI Reporting Policy
• Obesity in the United States: Defining the Problem
• Adolescent Obesity: Theories and Interventions
• Obesity in Children in the US
• Childhood Obesity: Issue Analysis
• Physical Exercises as Obesity Treatment
• Data Mining Techniques for African American Childhood Obesity Factors
• Approaches to Childhood Obesity Treatment
• Researching Childhood Obesity Issues
• Infant Feeding Practices and Early Childhood Obesity
• Prevalence of Obesity and Severe Obesity in U.S. Children
• Problem of Obesity: Analytic Method
• Obesity as National Practice Problem
• Childhood Obesity: Research Methodology
• Practice Problem of the Obesity in United States
• Exercise for Obesity Management: Evidence-Based Project
• Obesity in African-American Women: Methodology
• The Epidemiology of Obesity
• Pediatric Obesity Study Methodology
• Adult Obesity Causes & Consequences
• Community Health: Obesity Prevention
• Obesity Treatment in Primary Care: Evidence-Based Guide
• Childhood Obesity and Mothers’ Education Project
• Childhood Obesity Research Critiques
• Childhood Obesity: Medication and Parent Education
• Obesity Caused by Fast-Food as a Nursing Practice Issue
• Cardiometabolic Response to Obesity Treatment
• Motivational Interviewing in Obesity Reduction: Statistical Analysis
• Obesity Among the Adult Population: Research Planning
• Research and Global Health: Obesity and Overweight
• Childhood Obesity Interventions: Data Analysis
• Childhood Obesity as a Topic for Academic Studies
• Adolescent Obesity Treatment in Primary Care
• The Issues of Childhood Obesity: Overweight and Parent Education
• Obesity Reduction and Effectiveness of Interventions
• Childhood and Adult Obesity in the US in 2011-12
• Anti-Obesity Project’s Sponsors in the USA
• Obesity Prevention Advocacy Campaigns
• Childhood Obesity Study, Ethics, and Human Rights
• Childhood Obesity, Demographics and Environment
• Overweight and Obesity in 195 Countries Since 1980
• Childhood Obesity and American Policy Intervention
• Obesity in Miami as a Policy-Priority Issue
• Efficient Ways to Manage Obesity
• Childhood Obesity and Public Health Intervention
• Childhood Obesity and Healtcare Spending in the US
• Childhood Obesity, Medical and Parental Education
• Nursing Role in Tackling Youth Obesity
• Childhood Obesity: Problem Issues
• Adolescent Obesity and Parental Education Study
• Obesity Prevention and Patient Teaching Plan
• “Management of Obesity” by Dietz et al.
• Nutrition and Obesity: Management and Prevention
• Obesity, Diet Modification and Physical Exercises
• Obesity, Its Definition, Treatment and Prevention
• Childhood Obesity and Eating Habits in Low-Income Families
• Obesity: Society’s Attitude and Media Profiling
• Childhood Obesity and Family’s Responsibility
• Childhood Obesity: Parental Education vs. Medicaments
• Childhood Obesity and Health Promoting Schools Program
• Childhood Obesity Risks, Reasons, Prevention
• Fast Food as a Cause of Obesity in the US and World
• Obesity Prevention and Education in Young Children
• Childhood Obesity: The Relationships Between Overweight and Parental Education
• Obesity, Its Demographics and Health Effects
• Obesity Treatment: Surgery vs. Diet and Exercises
• Child Obesity as London’s Urban Health Issue
• Obesity Prevention in Young Children: Evidence-Based Project
• Advocacy Campaign: Childhood Obesity
• Prevalence of Childhood and Adult Obesity in the US
• The Role of Nurses in the Obesity Problem
• The Issue of Obesity in Youth in the U.S.
• The Role of Family in Childhood Obesity
• Obesity Among Children of London Borough of Southwark
• Childhood Obesity Risks and Preventive Measures
• Ways of Treating Obesity in Older Patients
• Obesity Interventions and Nursing Contributions
• Life Expectancy and Obesity Health Indicators
• The Overuse of Antibiotics and Its Role in Child Obesity
• Children and Adolescents With Obesity: Physical Examination
• Obesity in the United States: Learning Process
• Pharmacotherapy for Childhood Obesity
• “Let’s Move” Intervention for Childhood Obesity
• Obesity Prevention in Childhood
• Patient Education for Obesity Treatment
• Childhood Obesity Prevention Trends
• Obesity Prevention in Young Children in US
• Wellness, Academics & You: Obesity Intervention
• Childhood Obesity, Health and Psychological State
• Parents’ Education in Childhood Obesity Prevention
• Evidence Based Practice Related to Patient Obesity
• Childhood Obesity in the US
• Childhood Obesity and Its Solutions
• Obesity Problem among the Adult Population
• Obesity Education in Social Media for Children
• Childhood Obesity and Governmental Measures
• Childhood Obesity Research and Ethical Concerns
• Obesity, Its Contributing Factors and Consequences
• Obesity among the Adult Population
• Multimodal-Lifestyle Intervention for Obesity
• Technological Education Programs and Obesity Prevention
• Childhood Obesity and Independent Variable in Parents
• Childhood Obesity: A Global Public Health Crisis
• Childhood Obesity, Its Definition and Causes
• Public Health Initiative for Childhood Obesity
• Childhood Obesity in the US: Factors and Challenges
• Obesity: Genetic, Hormonal and Environmental Influences
• The Problem of Obesity in the USA
• Childhood Obesity in the USA
• Racial and Ethnic Trends in Childhood Obesity in the US
• Obesity in Miami-Dade Children and Adults
• Age and Gender in Childhood Obesity Prevention
• Childhood Obesity and Public Health Interventions
• Obesity in Florida and Prevention Programs
• Obesity in Afro-Americans: Ethics of Intervention
• Helping Children with Obesity and Health Risks
• The Role of Nurses in the Problem of Obesity
• Healthy Nutrition: Obesity Prevention in Young Children
• Myocardial Infarction, Obesity and Hypertension
• Childhood Obesity and Parent Education
• Obesity’s Effect on Children and Elderly People
• Childhood Obesity and Community Nursing Intervention
• Obesity Trends Among Non-Hispanic Whites and Blacks
• Family-Based Childhood Obesity and Parental Weight
• Childhood Obesity and Socio-Ecological Model
• Childhood Obesity and Depression Intervention
• Problem of the Childhood Obesity
• Advocacy Campaign: the Problem of Childhood Obesity
• Obesity in African Americans: Prevention and Therapy
• Childhood Obesity and Control Measures in the US
• Decreasing Obesity in Jewish Children
• Nutrition: Obesity Epidemics in America
• Fast Food and Obesity Link – Nutrition
• Dairy Products Consumption and Obesity – Nutrition
• Nutrition Issues: Obesity and Breastfeeding
• The Evidence of Association between Iron Deficiency and Childhood Obesity
• Food Allergies and Obesity
• Childhood Obesity: a Population Health Issue
• What Factors Causes Obesity?
• What Are Five Problems With Obesity?
• Can the Government Help the Obesity Issue?
• What Are the Three Dangers of Obesity?
• What Are Ten Health Problems Associated With Obesity?
• Are the Parents to Blame for Childhood Obesity?
• What Are the Social Effects of Obesity?
• Does Adolescent Media Use Cause Obesity and Eating Disorders?
• How Is Obesity Affecting the World?
• How Does Obesity Impact Quality of Life?
• Does Society Affect America’s Obesity Crisis?
• How Does Obesity Affect You Mentally?
• How Does Obesity Impact Children?
• How Does Obesity Affect Self-Esteem?
• How Does Obesity Cause Depression?
• Are First Generation Mexican Children More Prone to Obesity Than Their Second Generation Counterparts?
• Should Fast Food Companies Be Held Responsibility for Children’s Obesity?
• Does Obesity Cause Mood Swings?
• What Are the Causes and Effects of Childhood Obesity?
• Is Obesity a Mental or Physical Illness?
• What Comes First: Depression or Obesity?
• What Makes Obesity Dangerous?
• Which European Country Has the Highest Rate of Obesity?
• What Is the Obesity Rate in Africa?

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These essay examples and topics on Obesity were carefully selected by the StudyCorgi editorial team. They meet our highest standards in terms of grammar, punctuation, style, and fact accuracy. Please ensure you properly reference the materials if you’re using them to write your assignment.

This essay topic collection was updated on January 21, 2024 .

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Obesity articles from across Nature Portfolio

Obesity is a condition in which excess fat has accumulated in the body, such that it can have an adverse effect on health. Obesity is defined as a body mass index (BMI) of greater than 30 kg/m2.

The pressure not to eat

Eating requires the sensing in the stomach of not only nutrients, but also volume. A study in Nature Metabolism shows that stretch activation of PIEZO1 on X/A-like cells of the stomach reduces ghrelin production and secretion, which consequently reduces food intake.

• Choi Sang Daniel Lam
• M. Maya Kaelberer

Latest Research and Reviews

Gender-specific link between sleep quality and body composition components: a cross-sectional study on the elderly.

• Ali Kohanmoo
• Asma Kazemi
• Masoumeh Akhlaghi

Protein-truncating variants in BSN are associated with severe adult-onset obesity, type 2 diabetes and fatty liver disease

Analyses of whole-exome sequencing data identify rare loss-of-function variants in BSN associated with adult-onset obesity, type 2 diabetes and fatty liver disease, with stronger effect sizes than those observed for variants in known obesity risk genes such as MC4R.

• Maria Chukanova
• John R. B. Perry

The rostromedial tegmental nucleus gates fat overconsumption through ventral tegmental area output in male rats

• Florian Schoukroun
• Katia Befort
• Romain Bourdy

Obesity-related T cell dysfunction impairs immunosurveillance and increases cancer risk

Obesity represents a risk factor for cancer and compromises immune function, however the mechanisms linking the two together are not fully known. Here authors show in a mouse sarcoma model that obesity increases tumour incidence, impairs intra-tumoral T cell immunity but paradoxically increases sensitivity to immune therapy via impairing immunoediting.

• Alexander Piening
• Emily Ebert
• Ryan M. Teague

A negative feedback loop between TET2 and leptin in adipocyte regulates body weight

The epigenetic regulation in adipocytes during obesity remains poorly understood. Here, the authors demonstrate a negative feedback loop between TET2, a DNA demethylation enzyme, and leptin, an adipokine, in adipocytes, unveiling a compensatory mechanism by which the body counteracts the metabolic dysfunction induced by obesity.

• Jianfeng Song

TM4SF19-mediated control of lysosomal activity in macrophages contributes to obesity-induced inflammation and metabolic dysfunction

Adipose tissue adapts to overnutrition in a complex process, wherein specialized immune cells remove and replace dysfunctional and stressed adipocytes with new fat cells. Here, the authors show that the deletion of TM4SF19 expressed in lipid-associated macrophages, enhances the clearance of dying adipocytes, thereby improving local and systemic insulin sensitivity as well as energy expenditure.

• Cheoljun Choi
• Yujin L. Jeong
• Yun-Hee Lee

News and Comment

First US drug approved for a liver disease surging around the world

A therapy called resmetirom improves hallmarks of an obesity-linked condition that can lead to liver failure.

• Heidi Ledford

Blockbuster obesity drug leads to better health in people with HIV

Semaglutide reduces weight and fat accumulation associated with the antiretroviral regimen that keeps HIV at bay.

• Mariana Lenharo

Obesity management strategies should cut fat, not muscle

• Daan Kremer
• Dionne Sizoo
• André P. van Beek

Resisting weight gain with prebiotic fibre

Resistant starch is a prebiotic fibre that is fermented by the gut microbiota and leads to benefits for host physiology. A clinical trial in Nature Metabolism demonstrates weight loss when resistant starch was given to individuals with excess weight.

• Matthew M. Carter
• Sean P. Spencer

Phase I results for AMG 133

• Claire Greenhill

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How To Write A Strong Obesity Research Paper?

Table of Contents

Obesity is such a disease when the percent of body fat has negative effects on a person’s health. The topic is very serious as obesity poisons the lives of many teens, adults and even children around the whole world.

Can you imagine that according to WHO (World Health Organization) there were 650 million obese adults and 13% of all 18-year-olds were also obese in 2016? And scientists claim that the number of them is continually growing.

There are many reasons behind the problem, but no matter what they are, lots of people suffer from the wide spectrum of consequences of obesity.

Basic guidelines on obesity research paper

Writing any research paper requires sticking to an open-and-shut structure. It has three basic parts: Introduction, Main Body, and Conclusion.

According to the general rules, you start with the introduction where you provide your reader with some background information and give brief definitions of terms used in the text. Next goes the thesis of your paper.

The thesis is the main idea of all the research you’ve done written in a precise and simple manner, usually in one sentence.

The main body is where you present the statements and ideas which disclose the topic of your research.

In conclusion, you sum up all the text and make a derivation.

How to write an obesity thesis statement?

As I’ve already noted, the thesis is the main idea of your work. What is your position? What do you think about the issue? What is that you want to prove in your essay?

Answer one of those questions briefly and precisely.

Here are some examples of how to write a thesis statement for an obesity research paper:

• The main cause of obesity is determined to be surfeit and unhealthy diet.
• Obesity can be prevented no matter what genetic penchants are.
• Except for being a problem itself, obesity may result in diabetes, cancers, cardiovascular diseases, and many others.
• Obesity is a result of fast-growing civilization development.
• Not only do obese people have health issues but also they have troubles when it comes to socialization.

20 top-notch obesity research paper topics

Since the problem of obesity is very multifaceted and has a lot of aspects to discover, you have to define a topic you want to cover in your essay.

How about writing a fast food and obesity research paper or composing a topic in a sphere of fast food? Those issues gain more and more popularity nowadays.

A couple of other decent ideas at your service.

• The consequences of obesity.
• Obesity as a mental problem.
• Obesity and social standards: the problem of proper self-fulfilment.
• Overweight vs obesity: the use of BMI (Body Mass Index).
• The problem of obesity in your country.
• Methods of prevention the obesity.
• Is lack of self-control a principal factor of becoming obese?
• The least obvious reasons for obesity.
• Obesity: the history of the disease.
• The effect of mass media in augmentation of the obesity level.
• The connection between depression and obesity.
• The societal stigma of obese people.
• The role of legislation in reducing the level of obesity.
• Obesity and cultural aspect.
• Who has the biggest part of the responsibility for obesity: persons themselves, local authorities, government, mass media or somebody else?
• Why are obesity rates constantly growing?
• Who is more prone to obesity, men or women? Why?
• Correlation between obesity and life expectancy.
• The problem of discrimination of the obese people at the workplace.
• Could it be claimed that such movements as body-positive and feminism encourage obesity to a certain extent?

Best sample of obesity research paper outline

An outline is a table of contents which is made at the very beginning of your writing. It helps structurize your thoughts and create a plan for the whole piece in advance.

…Need a sample?

Here is one! It fits the paper on obesity in the U.S.

Introduction

• Hook sentence.
• Thesis statement.
• Transition to Main Body.
• America’s modern plague: obesity.
• Statistics and obesity rates in America.
• Main reasons of obesity in America.
• Social, cultural and other aspects involved in the problem of obesity.
• Methods of preventing and treating obesity in America.
• Transition to Conclusion.
• Unexpected twist or a final argument.
• Food for thought.

Specifics of childhood obesity research paper

A separate question in the problem of obesity is overweight children.

It is singled out since there are quite a lot of differences in clinical pictures, reasons and ways of treatment of an obese adult and an obese child.

Writing a child obesity research paper requires a more attentive approach to the analysis of its causes and examination of family issues. There’s a need to consider issues like eating habits, daily routine, predispositions and other.

Top 20 childhood obesity research paper topics

We’ve gathered the best ideas for your paper on childhood obesity. Take one of those to complete your best research!

• What are the main causes of childhood obesity in your country?
• Does obesity in childhood increase the chance of obesity in adulthood?
• Examine whether a child’s obesity affects academic performance.
• Are parents always guilty if their child is obese?
• What methods of preventing childhood obesity are used in your school?
• What measures the government can take to prevent children’s obesity?
• Examine how childhood obesity can result in premature development of chronic diseases.
• Are obese or overweight parents more prone to have an obese child?
• Why childhood obesity rates are constantly growing around the whole world?
• How to encourage children to lead a healthy style of life?
• Are there more junk and fast food options for children nowadays? How is that related to childhood obesity rates?
• What is medical treatment for obese children?
• Should fast food chains have age limits for their visitors?
• How should parents bring up their child in order to prevent obesity?
• The problem of socializing in obese children.
• Examine the importance of a proper healthy menu in schools’ cafeterias.
• Should the compulsory treatment of obese children be started up?
• Excess of care as the reason for childhood obesity.
• How can parents understand that their child is obese?
• How can the level of wealth impact the chance of a child’s obesity?

Childhood obesity outline example

As the question of childhood obesity is a specific one, it would differ from the outline on obesity we presented previously.

Here is a sample you might need. The topic covers general research on child obesity.

• The problem of childhood obesity.
• World’s childhood obesity rates.
• How to diagnose the disease.
• Predisposition and other causes of child obesity.
• Methods of treatment for obese children.
• Preventive measures to avoid a child’s obesity.

On balance…

The topic of obesity is a long-standing one. It has numerous aspects to discuss, sides to examine, and data to analyze.

Any topic you choose might result in brilliant work.

How can you achieve that?

Follow the basic requirements, plan the content beforehand, and be genuinely interested in the topic.

Option 2. Choose free time over struggle on the paper. We’ve got dozens of professional writers ready to help you out. Order your best paper within several seconds and enjoy your free time. We’ll cover you up!

How to Maintain Work with Studying and Stay Sane

55 Rare Topics For Persuasive Essays

Pop Culture Essay – Thoughts on Writing

Open Access is an initiative that aims to make scientific research freely available to all. To date our community has made over 100 million downloads. It’s based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. As PhD students, we found it difficult to access the research we needed, so we decided to create a new Open Access publisher that levels the playing field for scientists across the world. How? By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers.

We are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including Nobel Prize winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn citations and find new collaborators, meaning more people see your work not only from your own field of study, but from other related fields too.

Brief introduction to this section that descibes Open Access especially from an IntechOpen perspective

Want to get in touch? Contact our London head office or media team here

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Home > Books > Role of Obesity in Human Health and Disease

Top 100 Most Cited Studies in Obesity Research: A Bibliometric Analysis

Submitted: 07 June 2021 Reviewed: 14 June 2021 Published: 22 December 2021

DOI: 10.5772/intechopen.98877

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Obesity represents a major global public health problem. In the past few decades the prevalence of obesity has increased worldwide. In 2016, an estimated 1.9 billion adults were overweight; of these more than 650 million were obese. There is an urgent need for potential solutions and deeper understanding of the risk factors responsible for obesity. A bibliometric analysis study was designed to provide a comprehensive overview of top 100 most cited studies on obesity indexed in Web of Science database. The online search was conducted on June 6, 2021 using the keywords “Obesity” OR “Obese” OR “Overweight” in title filed with no limitations on document types or languages. The top 100 cited studies were selected in descending order based on number of citations. The obtained data were imported in to Microsoft Excel 2019 to extract the basic information such as title, authors name, journal name, year of publication and total citations. In addition, the data were also imported in to HistCite™ for further citation analysis, and VOSviewer software for windows to plot the data for network visualization mapping. The initial search retrieved a total of 167,553 documents on obesity. Of the total retrieved documents, only top 100 most cited studies on obesity were included for further analysis. These studies were published from 1982 to 2017 in English language. Most of the studies were published as an article (n = 84). The highly cited study on obesity was “Establishing a standard definition for child overweight and obesity worldwide: international survey” published in BMJ-British Medical Journal (Impact Factor 39.890, Incites Journal Citation Reports, 2021) in 2000 cited 10,543 times. The average number of citations per study was 2,947.22 (ranging from 1,566 to 10,543 citations). Two studies had more than 10,000 citations. A total of 2,272 authors from 111 countries were involved. The most prolific author was Flegal KM authored 14 studies with 53,558 citations. The highly active country in obesity research was United States of America. The included studies were published in 33 journals. The most attractive journal was JAMA-Journal of the American Medical Association (Impact Factor 56.272) published 17 studies and cited globally 51,853 times. The most frequently used keywords were obesity (n = 87) and overweight (n = 22). The countries with highest total link strength was United States of America (n = 155), followed by England (n = 140), and Scotland (n = 130). Our results show that most number of highly cited studies were published in developed countries. The findings of this study can serve as a standard benchmark for researchers to provide the quality bibliographic references and insights into the future research trends and scientific cooperation in obesity research.

• bibliometric analysis

Author Information

Tauseef ahmad *.

• Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China

*Address all correspondence to: [email protected];, [email protected]

1. Introduction

Obesity represents a major public health challenge, in the past few decades the prevalence of obesity has increased worldwide and associated with serious adverse health outcomes [ 1 , 2 ]. According to the statistics of World Health Organization, in 2016, an estimated 1.9 billion adults (18 years and older) were overweight, of these more than 650 million were obese. In 2019, 38 million children (under age of 5 years) were overweight or obese [ 3 ].

Obesity associated comorbidities including certain cancer, depression, fatty liver disease, hepatic steatosis, hyperlipidemia, hypertension, obstructive sleep apnea, orthopedic conditions, type 2 diabetes mellitus and social isolation [ 1 , 4 , 5 ]. There is an urgent need for potential solutions and deeper understanding of the risk factors responsible for obesity.

Bibliometric type studies are of great interest, conducted not only to present an overall overview of the published scientific literature but also critical and subjective summarization of the most influential scientific studies [ 6 , 7 , 8 ].

This study aimed to provide a comprehensive overview of top 100 most cited studies on obesity. The finding can serve as a standard benchmark for researchers and to provide the quality bibliographic references.

3.1 Study design

Bibliometric citation analysis study.

3.2 Searching strategy and database

On June 6, 2021 the online search was conducted on Web of Science, Core Collection database (Philadelphia, Pennsylvania, United State of America). The search keywords used were “Obesity” OR “Obese” OR “Overweight” in title filed with no limitations on documents types or languages. The top 100 cited studies were selected in descending order based on number of citations.

3.3 Data extraction

The obtained studies were imported in to Microsoft Excel 2019 to extract the basic information such as title, authors name, journal name, year of publication and total citations. In addition, the downloaded dataset were imported in to HistCite™ for further citation analysis.

3.4 Visualization network

Visualization network co-authorship countries and co-occurrence all keywords were plotted by using VOSviewer software version 1.6.15 ( https://www.vosviewer.com/ ) for windows.

4. Ethical approval

This study did not involve any human or animal subjects, thus, ethical approval was not required.

The initial search retrieved a total of 167,553 documents on obesity indexed in Web of Science database. Of the total retrieved documents, only top 100 most studies on obesity were included in this study. The included studies were published in English language. Most of the studies were published as an article (n = 84) followed by review (n = 14) and letter (n = 1). The average number of citations per study was 2,947.22, ranging from 1,566 to 10,543 citations.

The most cited study on obesity was “Establishing a standard definition for child overweight and obesity worldwide: international survey” published in BMJ-British Medical Journal in 2000 cited 10,543 times. Another study “Positional cloning of the mouse obese gene and its human homolog” published in Nature in 1994 was cited 10,214 times. A total of 10 studies were cited more than 5,000 times. Furthermore, 52 studies were cited at least 2,000 times, while the remaining studies were cited more than 1,500 times. The top 100 studies on obesity is presented in Table 1 .

5.1 Most prolific authors

A total of 2,272 authors contributed to top 100 most cited studies. The most prolific author was Flegal KM authored 14 studies with 53,558 citations, followed by followed by Carroll MD (n = 10, citations = 36,950), and Ogden CL (n = 9, citations = 34,784). Only nine authors authored at least five studies as shown in Table 2 . In addition, only 22 authors contributed in at least three studies.

Top 100 most cited studies on obesity.

Note: LCS: Local citation score; LCS/t: Local citation score per year; GCS: Global citation score; GCS/t: Global citation score per year.

Authors with at least 4 studies.

5.2 Most active countries

A total 111 countries were involved in top 100 most cited studies on obesity. The most active country was United States of America (studies contributed: 75, citations: 217,788), followed by United Kingdom (studies contributed: 18, citations: 57,015), Canada (studies contributed: 9, citations: 17,920), Japan (studies contributed: 9, citations: 26,695), France (studies contributed: 8, citations: 21,228), Sweden (studies contributed: 8, citations: 20,632), and Netherlands (studies contributed: 7, citations: 13,018) as shown in Table 3 . Only 21 countries were involved at least in four studies.

Country with at least 3 studies.

Note: LCS: Local citation score; GCS: Global citation score.

5.3 Journals

The top 100 most cited studies were published in 33 journals. The most attractive journal was JAMA-Journal of the American Medical Association published 17 studies and cited globally 51,853 times as shown in Table 4 . Only seven journals published at least 4 studies, six journals published two studies each, while the remaining journals published a single study each.

Journals published at least 4 studies.

Note: IF: Impact Factor, Incites Journal Citation Reports, 2021; Q: Quartile; LCS: Local citation score; LCS/t: Local citation score per year; GCS: Global citation score; GCS/t: Global citation score per year.

5.4 Commonly used keywords

A total of 366 keywords were used in the top 100 most cited studies. The most widely used keywords were obesity (n = 87) and overweight (n = 22) as shown in Table 5 .

The keywords used at least ten times.

5.5 Year of publication

The top 100 most cited on obesity were published from 1982 to 2017 as shown in Figure 1 . The highest number of studies were published in 2006 (n = 9, citations = 29,552) and 2007 (n = 7, citations = 19,035) as presented in Figures 1 and 2 .

Publication years of top 100 most cited studies in obesity research.

Total global citation score per year of top 100 most cited studies in obesity research.

5.6 Co-authorship countries network visualization

The minimum number of studies for a country was fixed at 3. Of the total countries, only 38 countries were plotted based on total link strength (TLS) as shown in Figure 3 . The countries with highest TLS were United States of America (155), England (140), and Scotland (130).

Co-authorship countries network visualization. Two clusters are formed; red color represents cluster 1 (24 items), and green color represents cluster 2 (14 items).

5.7 Co-occurrence all keywords network visualization

Of the total keywords, only 69 were plotted as shown in Figure 4 . The keyword body-mass index has the highest TLS 117, followed by overweight (65), adipose-tissue (56), prevalence (53), weight (52), and obesity (49).

Co-occurrence all keywords network visualization. Three clusters are formed; red color represents cluster 1 (29 items), green color represents cluster 2 (26 items), and blue color represents cluster 3 (14 items).

6. Discussion

In recent years, bibliometric type studies have been increased significantly, these studies not only recognize the most influential studies in certain area but also determine the research shift and other important insights into the bibliometric parameters. Globally, obesity is a major public health problem and the prevalence has increased in the past few decades. Therefore, this study was undertaken to recognize the most influential studies in obesity research and provide essential bibliographic information. To the best of our knowledge this is the first bibliometric analysis on top 100 most cited studies on obesity indexed in Web of Science database. The highly cited study in obesity research received a total of 10,543 citations. The study published in a highly rated journal in medicine had an impact factor of 39.890 and placed in quartile 1 (Q1) category. The study entitled “Establishing a standard definition for child overweight and obesity worldwide: international survey” provides cut off points for body mass index in childhood of six large nationally representative cross sectional growth studies [ 9 ].

Another study received a total of 10,218 citations. The study titled “Positional cloning of the mouse obese gene and its human homologue” discusses the potential role of obese gene and these genes may function as part of a signaling pathway from adipose tissue that acts to regulate the size of the body fat depot [ 10 ].

The top 100 most cited were published in 33 journals. The most attractive and core journals in obesity research were JAMA-Journal of the American Medical Association (n = 17), and Nature (n = 14) had an impact factor of 56.272, and 49.962 respectively. A total of 31 studies were published in these two journals with a total citations of 100,377, thus representing the quality of work and aiming of the authors for high impact factor journals. Influential studies on obesity were published in higher impact factor journals. Furthermore, studies published in higher impact factor journals are more likely to be cited by the scientific community. The impact factor shows importance and quality of a journal [ 109 ]. The top three authors based on number of studies in obesity research were Flegal KM (n = 14, citations = 53,558), followed by Carroll MD (n = 10, citations = 36,950), and Ogden CL (n = 9, citations = 34,784). In our study, the leading country was United States of America contributed in a total of 75 studies with a total citations of 217,788. The finding is in line with studies in other research areas [ 110 , 111 , 112 , 113 ].

7. Conclusion

This study provides a comprehensive information of the most cited studies in obesity research. Majority of the most cited studies were published by developed countries in higher impact factor journals. The current study might be helpful to researchers for insights into the future research trends and scientific cooperation.

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Obesity Research

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Over the years, NHLBI-supported research on overweight and obesity has led to the development of evidence-based prevention and treatment guidelines for healthcare providers. NHLBI research has also led to guidance on how to choose a behavioral weight loss program.

Studies show that the skills learned and support offered by these programs can help most people make the necessary lifestyle changes for weight loss and reduce their risk of serious health conditions such as heart disease and diabetes.

Our research has also evaluated new community-based programs for various demographics, addressing the health disparities in overweight and obesity.

NHLBI research that really made a difference

• In 1991, the NHLBI developed an Obesity Education Initiative to educate the public and health professionals about obesity as an independent risk factor for cardiovascular disease and its relationship to other risk factors, such as high blood pressure and high blood cholesterol. The initiative led to the development of clinical guidelines for treating overweight and obesity.
• The NHLBI and other NIH Institutes funded the Obesity-Related Behavioral Intervention Trials (ORBIT) projects , which led to the ORBIT model for developing behavioral treatments to prevent or manage chronic diseases. These studies included families and a variety of demographic groups. A key finding from one study focuses on the importance of targeting psychological factors in obesity treatment.

Current research funded by the NHLBI

The Division of Cardiovascular Sciences , which includes the Clinical Applications and Prevention Branch, funds research to understand how obesity relates to heart disease. The Center for Translation Research and Implementation Science supports the translation and implementation of research, including obesity research, into clinical practice. The Division of Lung Diseases and its National Center on Sleep Disorders Research fund research on the impact of obesity on sleep-disordered breathing.

Find funding opportunities and program contacts for research related to obesity and its complications.

Current research on obesity and health disparities

Health disparities happen when members of a group experience negative impacts on their health because of where they live, their racial or ethnic background, how much money they make, or how much education they received. NHLBI-supported research aims to discover the factors that contribute to health disparities and test ways to eliminate them.

• NHLBI-funded researchers behind the RURAL: Risk Underlying Rural Areas Longitudinal Cohort Study want to discover why people in poor rural communities in the South have shorter, unhealthier lives on average. The study includes 4,000 diverse participants (ages 35–64 years, 50% women, 44% whites, 45% Blacks, 10% Hispanic) from 10 of the poorest rural counties in Kentucky, Alabama, Mississippi, and Louisiana. Their results will support future interventions and disease prevention efforts.
• The Hispanic Community Health Study/Study of Latinos (HCHS/SOL) is looking at what factors contribute to the higher-than-expected numbers of Hispanics/Latinos who suffer from metabolic diseases such as obesity and diabetes. The study includes more than 16,000 Hispanic/Latino adults across the nation.

Find more NHLBI-funded studies on obesity and health disparities at NIH RePORTER.

Read how African Americans are learning to transform soul food into healthy, delicious meals to prevent cardiovascular disease: Vegan soul food: Will it help fight heart disease, obesity?

Current research on obesity in pregnancy and childhood

• The NHLBI-supported Fragile Families Cardiovascular Health Follow-Up Study continues a study that began in 2000 with 5,000 American children born in large cities. The cohort was racially and ethnically diverse, with approximately 40% of the children living in poverty. Researchers collected socioeconomic, demographic, neighborhood, genetic, and developmental data from the participants. In this next phase, researchers will continue to collect similar data from the participants, who are now young adults.
• The NHLBI is supporting national adoption of the Bright Bodies program through Dissemination and Implementation of the Bright Bodies Intervention for Childhood Obesity . Bright Bodies is a high-intensity, family-based intervention for childhood obesity. In 2017, a U.S. Preventive Services Task Force found that Bright Bodies lowered children’s body mass index (BMI) more than other interventions did.
• The NHLBI supports the continuation of the nuMoM2b Heart Health Study , which has followed a diverse cohort of 4,475 women during their first pregnancy. The women provided data and specimens for up to 7 years after the birth of their children. Researchers are now conducting a follow-up study on the relationship between problems during pregnancy and future cardiovascular disease. Women who are pregnant and have obesity are at greater risk than other pregnant women for health problems that can affect mother and baby during pregnancy, at birth, and later in life.

Find more NHLBI-funded studies on obesity in pregnancy and childhood at NIH RePORTER.

Learn about the largest public health nonprofit for Black and African American women and girls in the United States: Empowering Women to Get Healthy, One Step at a Time .

Current research on obesity and sleep

• An NHLBI-funded study is looking at whether energy balance and obesity affect sleep in the same way that a lack of good-quality sleep affects obesity. The researchers are recruiting equal numbers of men and women to include sex differences in their study of how obesity affects sleep quality and circadian rhythms.
• NHLBI-funded researchers are studying metabolism and obstructive sleep apnea . Many people with obesity have sleep apnea. The researchers will look at the measurable metabolic changes in participants from a previous study. These participants were randomized to one of three treatments for sleep apnea: weight loss alone, positive airway pressure (PAP) alone, or combined weight loss and PAP. Researchers hope that the results of the study will allow a more personalized approach to diagnosing and treating sleep apnea.
• The NHLBI-funded Lipidomics Biomarkers Link Sleep Restriction to Adiposity Phenotype, Diabetes, and Cardiovascular Risk study explores the relationship between disrupted sleep patterns and diabetes. It uses data from the long-running Multiethnic Cohort Study, which has recruited more than 210,000 participants from five ethnic groups. Researchers are searching for a cellular-level change that can be measured and can predict the onset of diabetes in people who are chronically sleep deprived. Obesity is a common symptom that people with sleep issues have during the onset of diabetes.

Find more NHLBI-funded studies on obesity and sleep at NIH RePORTER.

Learn about a recent study that supports the need for healthy sleep habits from birth: Study finds link between sleep habits and weight gain in newborns .

Obesity research labs at the NHLBI

The Cardiovascular Branch and its Laboratory of Inflammation and Cardiometabolic Diseases conducts studies to understand the links between inflammation, atherosclerosis, and metabolic diseases.

NHLBI’s Division of Intramural Research , including its Laboratory of Obesity and Aging Research , seeks to understand how obesity induces metabolic disorders. The lab studies the “obesity-aging” paradox: how the average American gains more weight as they get older, even when food intake decreases.

Related obesity programs and guidelines

• Aim for a Healthy Weight is a self-guided weight-loss program led by the NHLBI that is based on the psychology of change. It includes tested strategies for eating right and moving more.
• The NHLBI developed the We Can! ® (Ways to Enhance Children’s Activity & Nutrition) program to help support parents in developing healthy habits for their children.
• The Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project standardizes data collected from the various studies of obesity treatments so the data can be analyzed together. The bigger the dataset, the more confidence can be placed in the conclusions. The main goal of this project is to understand the individual differences between people who experience the same treatment.
• The NHLBI Director co-chairs the NIH Nutrition Research Task Force, which guided the development of the first NIH-wide strategic plan for nutrition research being conducted over the next 10 years. See the 2020–2030 Strategic Plan for NIH Nutrition Research .
• The NHLBI is an active member of the National Collaborative on Childhood Obesity (NCCOR) , which is a public–private partnership to accelerate progress in reducing childhood obesity.
• The NHLBI has been providing guidance to physicians on the diagnosis, prevention, and treatment of obesity since 1977. In 2017, the NHLBI convened a panel of experts to take on some of the pressing questions facing the obesity research community. See their responses: Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents (PDF, 3.69 MB).
• In 2021, the NHLBI held a Long Non-coding (lnc) RNAs Symposium to discuss research opportunities on lnc RNAs, which appear to play a role in the development of metabolic diseases such as obesity.
• The Muscatine Heart Study began enrolling children in 1970. By 1981, more than 11,000 students from Muscatine, Iowa, had taken surveys twice a year. The study is the longest-running study of cardiovascular risk factors in children in the United States. Today, many of the earliest participants and their children are still involved in the study, which has already shown that early habits affect cardiovascular health later in life.
• The Jackson Heart Study is a unique partnership of the NHLBI, three colleges and universities, and the Jackson, Miss., community. Its mission is to discover what factors contribute to the high prevalence of cardiovascular disease among African Americans. Researchers aim to test new approaches for reducing this health disparity. The study incudes more than 5,000 individuals. Among the study’s findings to date is a gene variant in African Americans that doubles the risk of heart disease.

Explore more NHLBI research on overweight and obesity

The sections above provide you with the highlights of NHLBI-supported research on overweight and obesity . You can explore the full list of NHLBI-funded studies on the NIH RePORTER .

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This paper is in the following e-collection/theme issue:

Published on 5.4.2024 in Vol 26 (2024)

Assessing the Clinical Efficacy of a Virtual Reality Tool for the Treatment of Obesity: Randomized Controlled Trial

Authors of this article:

Original Paper

• Dimitra Anastasiadou 1, 2 , PhD   ; 
• Pol Herrero 2 , MSc   ; 
• Paula Garcia-Royo 2 , MSc   ; 
• Julia Vázquez-De Sebastián 2, 3 , MSc   ; 
• Mel Slater 4, 5, 6 , DSC   ; 
• Bernhard Spanlang 4 , PhD   ; 
• Elena Álvarez de la Campa 4 , PhD   ; 
• Andreea Ciudin 7, 8, 9 , MD, PhD   ; 
• Marta Comas 7, 8, 9 , PhD   ; 
• Josep Antoni Ramos-Quiroga 2, 10, 11, 12 , MD, PhD   ; 
• Pilar Lusilla-Palacios 2, 10, 11, 12 , MD, PhD  

1 Department of Clinical and Health Psychology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain

2 Psychiatry, Mental Health and Addictions Research Group, Vall d´Hebron Research Institute, Barcelona, Spain

3 RE-FiT Barcelona Research Group, Vall d’Hebron Research Institute & Parc Sanitari Pere Virgili, Barcelona, Spain

4 Virtual Bodyworks S.L., Barcelona, Spain

5 The Institute of Neurosciences, Universitat de Barcelona, Barcelona, Spain

6 Department of Clinical Psychology and Psychobiology, Universitat de Barcelona, Barcelona, Spain

7 Endocrinology and Nutrition Department, Vall d’Hebron University Hospital, Barcelona, Spain

8 Vall d’Hebron Research Institute, Universitat Autònoma de Barcelona, Barcelona, Spain

9 Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain

10 Psychiatry Department, Vall d’Hebron University Hospital, Barcelona, Spain

11 Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain

12 Biomedical Network Research Centre on Mental Health (CIBERSAM), Barcelona, Spain

Corresponding Author:

Dimitra Anastasiadou, PhD

Department of Clinical and Health Psychology, Universitat Autònoma de Barcelona

Cerdanyola del Vallès, Barcelona

Bellaterra, Barcelona, 08193

Phone: 34 935813854

Email: [email protected]

Background: Virtual reality (VR) interventions, based on cognitive behavioral therapy principles, have been proven effective as complementary tools in managing obesity and have been associated with promoting healthy behaviors and addressing body image concerns. However, they have not fully addressed certain underlying causes of obesity, such as a lack of motivation to change, low self-efficacy, and the impact of weight stigma interiorization, which often impede treatment adherence and long-term lifestyle habit changes. To tackle these concerns, this study introduces the VR self-counseling paradigm, which incorporates embodiment and body-swapping techniques, along with motivational strategies, to help people living with obesity effectively address some of the root causes of their condition.

Objective: This study aims to assess the clinical efficacy of ConVRself (Virtual Reality self-talk), a VR platform that allows participants to engage in motivational self-conversations.

Methods: A randomized controlled trial was conducted with 68 participants from the bariatric surgery waiting list from the obesity unit of the Vall d’Hebron University Hospital in Barcelona, Spain. Participants were assigned to 1 of 3 groups: a control group (CG), which only received treatment as usual from the obesity unit; experimental group 1 (EG1), which, after intensive motivational interviewing training, engaged in 4 sessions of VR-based self-conversations with ConVRself, and underwent embodiment and body-swapping techniques; and experimental group 2 (EG2), which engaged in 4 VR-based sessions led by a virtual counselor with a prerecorded discourse, and only underwent the embodiment technique. In the case of both EG1 and EG2, the VR interventions were assisted by a clinical researcher. Readiness to change habits, eating habits, and psychological variables, as well as adherence and satisfaction with ConVRself were measured at baseline, after the intervention, 1 week after the intervention, and 4 weeks after the intervention.

Results: Regarding the primary outcomes, EG1 (24/68, 35%) and EG2 (22/68, 32%) showed significant improvements in confidence to lose weight compared to the CG (22/68, 32%) at all assessment points (β=−.16; P =.02). Similarly, EG1 demonstrated a significant increase after the intervention in readiness to exercise more compared to the CG (β=−.17; P =.03). Regarding the secondary outcomes, EG1 participants showed a significant reduction in uncontrolled eating (β=.71; P =.01) and emotional eating (β=.29; P =.03) compared to the CG participants, as well as in their anxiety levels compared to EG2 and CG participants (β=.65; P =.01). In addition, participants from the experimental groups reported high adherence and satisfaction with the VR platform (EG1: mean 59.82, SD 4.00; EG2: mean 58.43, SD 5.22; d =0.30, 95% CI −0.30 to 0.89).

Conclusions: This study revealed that using VR self-conversations, based on motivational interviewing principles, may have benefits in helping people with obesity to enhance their readiness to change habits and self-efficacy, as well as reduce dysfunctional eating behaviors and anxiety.

Trial Registration: ClinicalTrials.gov NCT05094557; https://www.clinicaltrials.gov/study/NCT05094557

Introduction

Obesity is defined as a severe, chronic, complex, and multifactorial disease with detrimental effects on the individual’s physical and psychological health [ 1 ]. Various treatment options are currently available for obesity, including psychological interventions, behavioral interventions for lifestyle modification, pharmacotherapy, and bariatric surgery (BS) [ 2 , 3 ]. All these treatments are taken into consideration in a process of shared decision-making to generate a patient-centered plan [ 4 ].

According to the National Institute for Health and Care Excellence [ 5 ], BS is a treatment option available for people living with obesity with BMI >40 kg/m 2 (or BMI between 35 kg/m 2 and 40 kg/m 2 if relevant comorbidity is present that could be improved with weight loss). People living with obesity who undergo BS require rigorous and comprehensive preoperative and postoperative monitoring and support. This support should emphasize the adoption and maintenance of a healthy lifestyle, as well as the identification of any clinical or psychological barriers that may hinder adherence to postoperative treatment [ 5 ]. However, when assessing the long-term maintenance of weight loss in obesity management, including through BS procedures, achieving lasting and sustainable changes in body composition remains highly challenging [ 6 ]. Therefore, for more effective obesity management, it is beneficial to incorporate multicomponent psychological interventions that aim to improve health as a whole, foster self-efficacy and self-esteem, and prioritize sustainable goals chosen by the individual [ 2 ].

In this context, motivational interviewing (MI) has gained considerable recognition as an effective approach to enhance treatment adherence [ 7 ] and has been included in the most recent psychological and behavioral recommendations for obesity management [ 2 ]. Specifically, MI is a counseling technique based on a person-centered approach that aims to help individuals to identify the discrepancies between their goals and current circumstances and empower them to explore new alternatives toward behavior change, thereby enhancing motivation and resolving ambivalence [ 8 ].

Furthermore, in recent years, telemental health care has emerged as a rapid and efficient means of establishing different communication channels between patients and mental health professionals and has been proven capable of transforming the availability, accessibility, and efficacy of psychological treatments [ 9 , 10 ], particularly during the COVID-19 pandemic and beyond [ 11 ]. Recent research findings provide support for the acceptability, feasibility, and preliminary effectiveness of using new technologies in the treatment of eating and weight disorders [ 12 - 14 ]. Specifically, the integration of virtual reality (VR) into psychological interventions for these conditions holds great promise in addressing some of the factors associated with the development or maintenance of the disorders. Through the provision of multisensory experiences, extrinsic feedback, and opportunities for embodiment, psychological VR interventions, which are rooted primarily in cognitive behavioral therapy principles, have demonstrated effectiveness as complementary tools in managing obesity and have been associated with promoting behavior changes and weight reduction, reducing binge eating episodes, and addressing body image concerns [ 12 , 14 - 18 ]. While these interventions focus on correcting specific behaviors and provide patients with a safe context to practice eating, emotional, and relational management, they do not adequately address some of the root causes of obesity, such as a lack of motivation to change, low self-efficacy, and the impact of weight stigma interiorization, which are factors that often hinder treatment adherence and long-term maintenance of lifestyle habits [ 19 , 20 ].

The SOCRATES project (Self Conversation in Virtual Reality Embodiment to Enhance Healthier Lifestyles Among People with Obesity), funded by the European Commission (951930), has introduced the VR self-counseling paradigm, referred to as ConVRself (Virtual Reality self-talk), which uses embodiment and body-swapping techniques. On the one hand, embodiment enables participants to experience the perceptual illusion that the virtual body is their own [ 21 , 22 ]. On the other hand, body swapping allows embodying alternatingly between 2 virtual bodies (or avatars , eg, one representing the self and the other a counselor) and maintaining a conversation between these 2 embodied perspectives [ 23 - 25 ]. In this study, ConVRself is used as a solution to help people living with obesity explore some of the root causes of their condition. Drawing on MI principles, this approach allows participants to engage in self-counseling through motivational conversations with themselves. In particular, participants are immersed in a VR environment that resembles a counselor’s office. First, they embody an avatar that looks like themselves (a look-alike ), and from this perspective, they explain their problem, goals, and/or aspirations to a counselor, seated in front of them across a table. Once they have stated their problem, participants switch to embody the counselor’s avatar. In this new perspective, they listen to a playback of their own words and watch the related body gestures made during their speech. After having listened, they can respond from the counselor’s embodied perspective. In this way, participants engage in a self-conversation by adopting 2 different embodied perspectives and maintaining a conversation between them. To ensure that these conversations remain motivational, participants undergo intensive training in MI before engaging in the VR self-counseling experience. The objective of these virtual self-conversations has been to address the following challenges that people living with obesity face: (1) to raise awareness of their actual condition, (2) to better understand and address the impact of weight bias interiorization, and (3) to increase their self-efficacy by setting realistic goals in line with their values.

In this randomized controlled trial (RCT) based on the protocol described in the study by Anastasiadou et al [ 26 ], our primary objective was to evaluate the clinical efficacy of the ConVRself platform under 3 different conditions: one that uses the embodiment and body-swapping techniques together with the MI training, one only using the embodiment technique, and a group receiving treatment as usual (TAU).

We hypothesized that participants who used ConVRself with embodiment and body-swapping techniques, along with the MI training, would show greater improvement in the primary outcomes (motivation to lose weight and exercise more) as well as the secondary outcomes (lifestyle habits and psychological well-being) from baseline (T0) to the 3 postintervention assessments compared to participants who used ConVRself with only the embodiment element or the TAU group.

Ethical Considerations

This study was approved by the Clinical Research Ethics Committee and Research Projects Committee of the Vall d’Hebron University Hospital (VHUH). The study protocol was preregistered at ClinicalTrials.gov (NCT05094557) and published at BMJ Open [ 26 ]. Before enrollment in the study, written informed consent was obtained from all participants. To maintain confidentiality, each participant was assigned a numerical code. No compensation was provided for participating in the study.

Recruitment

Participants from the BS waiting list from the obesity unit of the VHUH within the national health system were assessed for eligibility between December 2021 and April 2023. To be eligible for inclusion, participants had to meet the following criteria: aged between 18 and 65 years; BMI ≥30 kg/m 2 ; receiving ambulatory treatment at the VHUH; not undergoing any other concurrent treatment specifically related to their obesity condition from other centers; possessing minimal digital skills, which means being able to use a digital device (smartphone, tablet, or computer) to make telephone calls and have video conversations via the internet, send or receive emails, and search for information about products and (health) services; demonstrable oral and written understanding of the Spanish language; and willingness to provide informed consent to participate. As stated in the usability study conducted by Anastasiadou et al [ 27 ], the BMI criterion was revised to include only a minimum threshold of ≥30 kg/m 2 for eligibility to participate in the study, contrary to the BMI criteria originally set in the study protocol (BMI between ≥30 kg/m 2 and ≤55 kg/m 2 ) [ 26 ]. Participants were not eligible if they met ≥1 of the following exclusion criteria: presence of an eating disorder in the last 2 years, nonstabilized severe mental disorder that could interfere with the successful implementation of the research protocol (ie, psychosis, depression with suicidal risk, alcohol or drug abuse, and psychotic or manic symptoms), intellectual disability or any major illness seriously affecting cognitive performance (ie, neurological disorders), and personal history of epilepsy (to avoid the potential risk of triggering seizures in this population). Of the 94 participants assessed for eligibility, 68 (72%) were recruited to participate in the study ( Figure 1 ). Details regarding the sample size calculation are available in the protocol (32).

We conducted an RCT with 3 parallel groups (experimental group 1 [EG1], experimental group 2 [EG2], and a control group [CG]) with a 1:1:1 allocation ratio. Measurements were carried out at 4 time points: T0, after the intervention (T1), 1 week after the intervention (T2), and 4 weeks after the intervention (T3).

Participant data were managed and automatically distributed using REDCap (Research Electronic Data Capture; Vanderbilt University) [ 28 , 29 ], hosted at the Vall d’Hebron Research Institute in Barcelona, Spain. The data collected at T0, T1, T2, and T3 were obtained through web-based self-report instruments completed by participants using REDCap. In addition, the research team members measured participants’ weight and height to calculate their BMI.

Potential eligible participants for the project were referred to the research team by health care professionals from the VHUH obesity unit. These participants received TAU provided by the obesity unit, which included regular medical, nutritional, and psychiatric follow-ups conducted by specialists at the hospital. Eligible participants were contacted via telephone by a research team member who provided information about the study. If they agreed to participate in the study, an appointment was scheduled at the hospital. During this appointment, a clinical interview was conducted by the team member to confirm the participants’ eligibility. Sociodemographic and clinical variables were also collected, and weight and height measurements were taken. Once the necessary data were collected, participants were randomly distributed in 1 of the 3 groups (EG1, EG2, and CG) using REDCap and were then informed about the outcome of the randomization. Next, they were asked to complete the web-based T0 assessment within the following week, which was managed by the automatic email distribution facilitated by REDCap. In addition, participants were asked to watch a 30-minute video with psychoeducational advice created by the research team. This video provided information about obesity and the promotion of healthy lifestyle habits. It was shared to ensure that all participants had a similar background concerning the concept of obesity and some knowledge about healthy lifestyle habits.

After the completion of the T0 assessment, participants received their assigned interventions based on their respective groups for a period of 10 weeks. The timeline of the assessments is depicted in Multimedia Appendix 1 . The T1 assessment took place during week 6, T2 assessment during week 7, and T3 assessment during week 10 using the same automatic email distribution as with the T0 assessment. Furthermore, REDCap implemented an automatic alert system to remind participants to complete the questionnaires if they had not done so at the scheduled time. Finally, participants who underwent BS during the course of the 4 exposures (for details, refer to the Experimental Groups subsection) were still requested to respond to the T1 assessment, even if they did not complete the full intervention.

Interventions

Cg participants.

After completing the T0 assessment, CG participants did not receive any intervention besides the TAU provided by the VHUH obesity unit and the psychoeducational video.

Experimental Groups

Interventions overview.

After completing the T0 assessment, EG1 participants underwent a 1-day MI training at the hospital facilities. The training lasted 4 hours and focused on developing basic MI skills. In addition, participants had an individual coaching session via telephone 1 week after the training. The initial in-person training session was led by an expert in MI (author PLP), while the follow-up sessions were carried out by the other members of the research team. At the end of the initial session, 3 photographs (2 from the front and 1 from the side) were taken of each participant to create their look-alike avatars, while their counselor’s avatar was designed according to each participant’s preferences concerning sex, age, and body shape. EG2 participants did not receive any MI training. Instead, they were invited to the hospital facilities where a research team member took photographs for the creation of their avatars.

Two weeks later, EG1 and EG2 participants engaged in 4 VR scenarios at the hospital facilities, assisted by a team clinical researcher. Scenarios were distributed in weekly sessions, each lasting 30 minutes, over a period of 4 weeks. After each exposure, satisfaction and adherence to the VR experiments were assessed for both groups using a semistructured interview designed by the research team. In addition, several self-report questionnaires were administered to the participants, including the readiness ruler (RR), the Suitability Evaluation Questionnaire (SEQ), and the Body Ownership Questionnaire (BOQ). For more detailed information, refer to the Measures subsection.

Specifically, the exposures of the 2 groups are detailed in the following subsections.

EG1 Participants

In each of the 4 scenarios, participants had a self-conversation using the embodiment and body-swapping techniques. Specifically, for exposures 1, 2, and 4, participants alternated between their look-alike avatar and the avatar of a counselor. During exposure 3, they alternated between their own avatar and an avatar representing their future self—a representation depicting their future self after adopting a healthier lifestyle 5 years from the present. When embodying the counselor and the future-self avatars, participants applied the MI techniques they had learned during the intensive training.

Exposure 1: Embodied Discussion About Problems and Solutions

The purpose of this scenario was to facilitate motivational self-conversations between the participant and their counselor about the lifestyle changes they planned to achieve in terms of eating healthier and being more physically active.

Exposure 2: Overcoming Self-Stigmatization

The objective of this intervention was to explore and address the participants’ subjective weight stigma experiences and their interiorization through motivational self-conversations between the participant and their counselor.

Exposure 3: Illustrating the Possibility of Autonomy

The objective of this intervention was to explore, through motivational self-conversations with participants’ future selves, how these future selves successfully achieve the goals that participants set in the present and to identify any barriers encountered during the process.

Exposure 4: Summing Up

Participants started their self-conversations by sharing the insights they gained from the previous exposures. In addition, they reflected on how these insights could be effectively implemented in their daily lives.

EG2 Participants

EG2 participants received a traditional counseling approach in a virtual setting. In all 4 exposures, participants were only embodied in their own look-alike avatars. First, participants engaged in a prerecorded discourse, conducted by a virtual counselor, that posed open-ended questions to which the participants responded. Second, the virtual counselor provided general and prerecorded advice that could be beneficial for the participants in promoting a healthier lifestyle.

For this exposure, the virtual counselor asked about the perceived barriers that participants faced when trying to adopt a healthier lifestyle and then provided practical recommendations to help overcome these barriers and facilitate the adoption of a healthier lifestyle. Examples of these recommendations are as follows:

Thank you for sharing your goals with me. Next, I’m going to give you some simple tips that can help improve your lifestyle and overall well-being. First of all, try to avoid miracle diets as none of them work in the medium and long term...Second, make healthy choices regarding your diet. Certain foods should be prioritized, others limited, and some replaced with healthier alternatives.

In this exposure, the participants shared their subjective experiences of body size discrimination, and the virtual counselor offered practical advice about how to deal with them. Some examples of the advice given are as follows:

Despite the enticing advertisements encouraging you to believe that image is everything, never forget that your appearance is just one aspect of who you are. Try to develop your sense of identity based on all the things you can do and the person you are deep within, despite inhabiting a larger body...Secondly, appreciate and take care of your body—a body that, when healthy, can accomplish many things.

In this exposure, the conversation focused on discussing the potential positive effects that people living with obesity may experience when they adopt healthier behaviors that prioritize their overall health, rather than solely focusing on weight. An example of such a conversation is as follows:

Given that many factors influence your health status, some of which are beyond your control, one important step you can take to promote good health while living in a larger body is to adopt healthy eating and exercise habits, along with activities that foster social support, without solely focusing on weight loss.

Participants engaged in a conversation during which the virtual counselor provided a general summary of the main concepts explained in the previous exposures.

Technical Features of the VR System

The VR system used in the study consisted of both hardware and software components. The VR hardware used was the Meta Quest 2, which is a stand-alone headset developed by Reality Labs (Meta Platforms, Inc). The main part of the hardware consists of a head-mounted display worn by the participants. This head-mounted display has a vision- and inertia-based inside-out tracking system that allows precise tracking of the user’s head movements. In addition, the Meta Quest 2 comes equipped with hand controllers that enable interaction within the virtual environment. Finally, the Meta Quest 2 also incorporates a built-in processor that generates stereoscopic images and spatialized audio. The headset runs the ConVRself application, which was developed using the Unity 3D development environment (Unity Technologies).

ConVRself Software

The VR software ConVRself, developed by Virtual Bodyworks SL, displays 3D scenarios and virtual human representations. This VR application enables participants to have self-conversations by embodying their look-alike avatar and another avatar alternatingly. The software generates scenarios that have 3 stages: calibration, tutorial, and experience. In the first stage, calibration, participants wore a VR headset and held VR controllers. The system used this setup to calculate an internal human representation that synchronized the movements of the embodied avatar with the participants’ own movements. During the next 2 stages, tutorial and experience, participants were immersed in the virtual environment, embodying their look-alike avatar from a first-person perspective. To enhance the sense of embodiment, they could see themselves reflected in a virtual mirror on their left. To watch a video showing how ConVRself works, please refer to the supplementary materials in the study by Anastasiadou et al [ 26 ]. Specifically in the tutorial stage, participants had to follow detailed audio instructions provided by the application to get used to the platform before the actual experience. In addition, during this stage, participants underwent the embodiment technique to foster the illusion that the virtual body represented their own. Finally, in the experience stage, participants engaged in different exposures.

To minimize the risks associated with COVID-19, the research team followed a safety protocol that included wearing masks, carrying out regular hand disinfection, and using the CX1 decontamination system (Cleanbox Technology) to clean the Meta Quest 2 headsets and controllers used in the study.

Primary Outcomes

The primary outcomes (motivation to lose weight and exercise more) were assessed using 2 measurement tools: the RR [ 8 ] and the Stages of Change Questionnaire for Weight Management (S-Weight) and Processes of Change Questionnaire for Weight Management (P-Weight) [ 30 ]. The RR is a visual analog scale ranging from 1 to 10 that assessed participants’ readiness for, confidence about, and perception of the importance of changing behavior with regard to 2 specific areas: (1) achieving a healthy weight and (2) exercising more. The S-Weight questionnaire consists of 5 mutually exclusive items that aim to allocate participants to 1 of the 5 stages of change in weight management according to the transtheoretical model: precontemplation, contemplation, preparation, action, and maintenance. The P-Weight questionnaire is a 5-point Likert scale (ranging from strongly disagree to strongly agree ) consisting of 34 items developed to assess 4 processes of change for weight management: (1) emotional re-evaluation (13 items), (2) weight management actions (7 items), (3) environmental restructuring (5 items), and (4) weight consequences evaluation (9 items). The scores of each subscale were summed to obtain a total score and were then transformed on a new scale ranging from 0 to 100. The Spanish version of P-Weight showed an adequate internal consistency (Cronbach α coefficients ranged from 0.78 to 0.96 in both individuals with normal weight and individuals with overweight and obesity) [ 30 ]. The Cronbach α values in this study for the P-Weight subscales were 0.74 for emotional re-evaluation, 0.74 for weight management actions, 0.76 for environmental restructuring, and 0.76 for weight consequences evaluation.

Secondary Outcomes

Eating habits.

The Three-Factor Eating Questionnaire–Revised 18 items (TFEQ-R18) [ 31 ] is a self-report questionnaire designed to measure 3 aspects of eating behavior: (1) cognitive restraint (CR; 6 items), (2) uncontrolled eating (UE; 9 items), and (3) emotional eating (EE; 3 items). Participants responded to each item on a 4-point Likert scale ranging from definitely true to definitely false . The total scores of each subscale were obtained by summing the scores of individual items. The Spanish version of the TFEQ-R18 [ 32 ] showed good internal consistency (Cronbach α coefficients ranged from 0.75 to 0.87) in a sample of young and healthy adults. The Cronbach α values in this study for each subscale were 0.59 for CR, 0.87 for UE, and 0.78 for EE.

The Eating Habits Questionnaire [ 33 ] is a self-report questionnaire with 37 items, each rated using a 5-point Likert scale ranging from never to always . This questionnaire measures eating habits across 8 different spheres: (1) sugar intake (4 items), (2) healthy eating (9 items), (3) physical activity (3 items), (4) diet caloric intake (5 items), (5) psychological well-being (3 items), (6) types of aliments (5 items), (7) knowledge and control (5 items), and (8) alcohol intake (2 items). A total score was obtained as the average of the scores from the 8 spheres. The Cronbach α coefficient for the complete questionnaire was 0.87 and ranged from 0.58 to 0.94 for the different spheres in a Spanish sample of adult participants living with overweight and obesity [ 33 ]. In this study, the Cronbach α value was 0.88.

Psychological Variables

Psychological functioning was estimated with the Hospital Anxiety and Depression Scale (HADS) [ 34 ]. The HADS is a self-report 14-item questionnaire (7 items for anxiety and 7 items for depression). Participants rated each item on a 4-point Likert scale to indicate the presence and severity of anxiety and depression symptoms. The total scores of each factor were obtained by summing the scores of individual items. The Spanish version showed high internal consistency, with a Cronbach α value of 0.86 for the 2 factors in a sample of patients and healthy controls [ 35 ]. The Cronbach α values in this study were 0.77 for the depression subscale and 0.79 for the anxiety subscale.

Body satisfaction was measured using the 10-item validated Spanish version of the Body Shape Questionnaire [ 36 ]. This self-report scale is rated using a 6-point Likert scale ranging from never to always . A total score was obtained by summing the scores of individual items. In this study, the Cronbach α value for this questionnaire was 0.89.

The Modified Weight Bias Internalization Scale (WBIS-M) [ 37 ] was used to assess weight bias interiorization. The WBIS-M is a self-report 11-item unidimensional scale rated using a 7-point scale ranging from strongly disagree to strongly agree . A total score was obtained as the sum of the scores of individual items. The Cronbach α coefficient for the complete questionnaire ranged from 0.93 to 0.94 in a sample of Spanish adults [ 37 ]. The Cronbach α value for this questionnaire in this study was 0.86.

The Cognitive Reserve Questionnaire [ 38 ] was used to measure participants’ cognitive reserve. This self-report questionnaire consists of 8 items that evaluate aspects generally related to cognitive reserve, such as educational status (own and parental), occupational status, completion of training courses, musical training, and language proficiency. The total score was obtained by summing the item scores. The Cronbach α value for this questionnaire in this study was 0.63.

Adherence and Satisfaction Regarding VR Experiments

To measure satisfaction, acceptance, and security regarding the use of the ConVRself platform, we used the SEQ [ 39 ]. The SEQ is a 14-item questionnaire, with 13 items rated on a 5-point Likert scale ranging from not at all to very much , as well as a last open-ended question where participants can provide suggestions and additional feedback. For the specific purposes of this study, the word “rehabilitation” in item 11 was replaced by “obesity treatment.” The total score was obtained by summing the scores of the first 13 items. Validation studies of the SEQ showed an acceptable internal consistency, with a Cronbach α value of 0.70 in samples of individuals with different physical pathologies. The Cronbach α value of the SEQ in this study was 0.61.

The BOQ evaluates the subjective illusion of body ownership in a VR context through a 7-point Likert scale ranging from strongly disagree to strongly agree . Specifically, the 4 questions of this scale were obtained from a previous study evaluating ConVRself [ 25 ]. The questionnaire assesses body ownership when (1) looking down at the virtual body, (2) observing oneself in a virtual mirror, (3) perceiving body movements, and (4) recognizing oneself. These questions were asked for the participants’ own avatar as well as the counselor’s avatar (except for the question regarding the self-recognition item, which was only asked for the participants’ own avatar). For EG2 participants, the questions were asked exclusively for their own avatar because they did not experience the body-swapping technique. For more information about particular items, please refer to the protocol published in the study by Anastasiadou et al [ 26 ].

Along with the questionnaires, a brief interview was conducted after each exposure to assess participants’ satisfaction with the VR experience and acceptability of ConVRself.

Statistical Analysis

Initial analyses involved comparisons of EG1, EG2, and CG participants on sociodemographic and clinical characteristics, adherence (dropout analysis), and assessment variables at the T0 level. Subsequent assessments examined those participants who completed the long-term follow-up assessment and those who did not on the same sociodemographic and clinical characteristics and outcome variables at T0. Depending on variable types or objective, various statistical tests were used: the Shapiro-Wilk test for assessing the normality of the distribution, a 1-way ANOVA with group as a factor for normally distributed variables, the Friedman test for variables with non-normal distributions, and the chi-square test for qualitative variables.

To handle missing data within questionnaires, passive multiple imputation was used. This approach updates total scores based on recent imputed values at the item level, thereby ensuring complete data for analysis [ 40 , 41 ].

Analyses for our primary and secondary outcomes were tested with 2-level hierarchical linear models (HLMs). These models were implemented with group (EG1, EG2, and CG) and time (T0, T1, T2, and T3 for the RR; T0, T1, and T3 for other variables) as fixed factors and participants nested within time as a random factor. For model adjustment, we used restricted maximum likelihood as the estimation method and the scaled identity as the error covariance structure. Potential moderators (such as age, sex, BMI at T0, time of treatment at the VHUH obesity unit, the presence of physical comorbidities, current mental illness, and Cognitive Reserve Questionnaire scores at T0) were examined. All covariables were grand mean centered.

An intent-to-treat (ITT) analysis was conducted using the available data of all participants for outcome estimation. This analysis, leveraging the ability of HLMs to integrate missing data, offers a more realistic, unbiased analysis compared to traditional methods [ 42 ]. To ensure the robustness of the results obtained from the primary and secondary ITT analyses, we also conducted a per-protocol analysis. This analysis included only participants who completed the RCT. Results from the per-protocol analysis were reported if they differed from the results of the aforementioned ITT analyses.

Effect sizes were calculated and reported using R 2 marginal (variance explained by the fixed effects) and R 2 conditional (variance explained by the entire model, both fixed and random effects) [ 43 , 44 ] for HLMs and Cohen d for comparison between the experimental groups in VR technique. Their magnitude was interpreted according to the Cohen guidelines where R 2 =0.01 or d ≤0.2 represents a small effect, R 2 =0.06 or d =0.5 represents a medium effect, and R 2 ≥0.14 or d ≥0.8 represents a large effect [ 45 ].

Analyses were conducted using SPSS (version 29.0; IBM Corp) and RStudio (version 2022.12.0; Posit Software, PBC), with the mice package from RStudio [ 46 ] for passive multiple imputation. A 2-tailed significance level of .05 was applied to all statistical tests.

Sample Description

As shown in Figure 1 , of the initial 94 participants assessed for the study, 19 (20%) were excluded before the randomization, resulting in 75 (80%) participants being enrolled and randomized. However, after receiving the T0 assessment, of the 75 participants, 7 (9%) decided not to continue participating; therefore, 68 (91%) participants completed the T0 assessment and were allocated to 1 of the 3 groups: EG1 (n=24, 35%), EG2 (n=22, 32%), and CG (n=22, 32%).

In EG1, of the 24 participants, 22 (92%) received the allocated intervention, of whom 16 (73%) completed the 4 exposures. At T1, 77% (17/22) responded; and at T2 and T3, 73% (16/22) responded. In EG2, of the 22 participants, 21 (95%) received the allocated intervention, of whom 13 (62%) completed the 4 exposures. At T1, 67% (14/21) responded; at T2, 57% (12/21); and at T3, 62% (13/21). Regarding the CG, of the 22 participants, at T1, 18 (82%) responded; at T2, 16 (73%); and at T3, 16 (73%). The adherence analysis did not reveal statistical differences among the groups ( χ 2 6 = 4.6; P =.60).

The sociodemographic and clinical characteristics of the sample are presented in Multimedia Appendix 2 . Participants had a mean age of 44.22 (SD 10.30) years, a mean BMI of 43.58 (SD 5.96) kg/m 2 , and had been receiving treatment at the VHUH obesity unit for an average of 21.62 (SD 11.18) months. Most of the participants were female individuals (54/68, 79%), Spanish citizens (51/68, 75%), employed either part time or full time (39/68, 57%), and lived with their family (50/68, 74%). Regarding clinical data, most of the participants had physical comorbidities (58/68, 85%), with pain and cardiovascular problems being the most prevalent, and no current mental illness (53/68, 78%). When comparing all groups on sociodemographic variables, significant differences were found regarding sex ( χ 2 2 =8.4; P =.02), the presence of current mental illness ( χ 2 2 =8.4; P =.02), and physical comorbidities ( χ 2 2 =6.0; P =.05).

Furthermore, there were no differences among the groups in any assessment variable at the T0 level ( P >.05). Descriptive results of the primary and secondary outcomes of all participants, and separately for each group, are presented in Multimedia Appendices 3 and 4 .

When comparing participants who completed the long-term follow-up assessments and those who did not, significant results were found regarding the following variables: (1) age (t 65 =−2.68; P =.009), (2) TFEQ-R18 CR (t 66 =−2.74; P =.008), (3) Eating Habits Questionnaire total score (t 64 =−3.23; P =.002), and (4) S-Weight ( χ 2 3 =16.3; P =.001). More precisely, participants with a higher mean age (46.4, SD 10.3) were more likely to complete the follow-up assessments than younger participants (mean age 39.7, SD 8.8). In addition, participants who completed all assessment points had significantly higher means in the TFEQ-R18 CR subscale and Eating Habits Questionnaire total score, and a higher percentage of them were at the maintenance stage (S-Weight), compared to those who did not complete all assessments.

The results of the analysis examining the effects of group condition and time are shown in Table 1 . The HLMs revealed significant results for some RR scales, while no effects were observed for group versus time and time for P-Weight and S-Weight.

a All hierarchical linear models were estimated with time and group as fixed effects; participant nested time as random effects; and age, sex, BMI at baseline, time of treatment at the obesity unit of the hospital, the presence of physical comorbidities, current mental illness, and Cognitive Reserve Questionnaire scores at baseline as covariables.

b Numbers presented are estimated values.

c R 2 marginal refers to the amount of variance explained by the fixed effects.

d R 2 conditional refers to the amount of variance explained by the entire model, both fixed and random effects.

e RR: readiness ruler.

f P-Weight: Processes of Change Questionnaire for Weight Management.

g S-Weight: Stages of Change Questionnaire for Weight Management.

h TFEQ-R18: Three-Factor Eating Questionnaire–Revised 18 items.

i HADS: Hospital Anxiety and Depression Scale.

j BSQ-10: Body Shape Questionnaire, 10-item version.

k WBIS-M: Modified Weight Bias Internalization Scale.

RR Analysis

First, regarding confidence to lose weight , the HLM revealed a significant group versus time effect (β=−.16; P =.02). Post hoc comparisons revealed that both EG1 and EG2 showed significant differences compared to the CG at T0 versus T1, T0 versus T2, and T0 versus T3. This notable increase in confidence to lose weight for both groups can be seen in Figure 2 . Second, a significant group versus time effect for readiness to exercise more (β=−.17; P =.03) was found, with post hoc comparisons showing a significant increase for EG1 compared to CG at T0 versus T2. The significant interaction effect is represented graphically in Figure 2 , where we can also see how the different group conditions evolve through the different time measures. Finally, participants from all groups had a significant improvement in their confidence to exercise more between T0 and T1 and between T0 and T3 (β=.55; P =.003) and in their readiness to lose weight between T0 and T1, T0 and T2, and T0 and T3 (β=.36; P =.01).

The HLMs revealed significant results for some TFEQ-R18 and HADS subscales, while no significant group versus time and time effects were found for the Eating Habits Questionnaire, Body Shape Questionnaire, and WBIS-M. The significant interaction effect for the TFEQ-R18 and HADS subscales is represented graphically in Multimedia Appendix 5 .

TFEQ-R18 Analysis

The HLMs revealed a significant time×group effect for the UE (β=.71; P =.01) and EE subscales (β=.29; P =.03). Post hoc comparisons revealed consistently lower levels of UE for EG1 versus CG across all time measures, and for EE, a reduction for EG1 versus CG at T0 versus T3.

HADS Analysis

A significant group versus time effect was found for the anxiety subscale (β=.65; P =.01). In particular, post hoc comparisons revealed greater reductions in anxiety levels between T0 and T1 for EG1 compared to EG2 and CG. For the depression subscale, only a significant time effect was found (β=1.07; P =.04), indicating a decrease in depression levels across all groups over time.

The HLM per-protocol analysis revealed consistent results with the ITT analysis, showing no significant differences between the 2 approaches.

Adherence and Satisfaction Regarding VR Experiments (SEQ and BOQ)

For the SEQ, the experimental groups demonstrated high suitability scores with the VR platform (EG1: mean 59.82, SD 4.00; EG2: mean 58.43, SD 5.22; d =0.30, 95% CI −0.30 to 0.89).

Regarding the BOQ, both groups displayed average positive agreement scores, showing higher mean in EG1 than in EG2. Specifically, from the perspective of their look-alike avatar, for item 1 (looking down at the virtual body), EG1 had a mean of 2.09 (SD 1.17), while EG2 had a mean of 0.05 (SD 2.12; d =1.18, 95% CI 0.53-1.81); for item 2 (observing oneself in a virtual mirror), EG1 scored a mean of 2.34 (SD 0.70), and EG2 scored a mean of 1.53 (SD 1.58; d =0.66, 95% CI 0.05-1.26); for item 3 (perceiving body movements), EG1 scored a mean of 2.56 (SD 0.45), and EG2 scored a mean of 2.14 (SD 0.97; d =0.56, 95% CI −0.54 to 1.14); and for item 4 (recognizing oneself), EG1 scored a mean of 1.53 (SD 1.31), and EG2 scored a mean of 1.36 (SD 1.57; d =0.12, 95% CI −0.47 to 0.71). From the counselor’s perspective, EG1 also demonstrated average agreement scores: (1) looking down at the virtual body: mean 1.72 (SD 1.25); (2) observing oneself in a virtual mirror: mean 1.99 (SD 0.81); and (3) perceiving body movements: mean 2.25 (SD 0.64).

Principal Findings

This study is focused on assessing the clinical efficacy of the ConVRself platform in tackling some of the root causes of obesity. The findings of this study confirmed our hypothesis, indicating that ConVRself with embodiment and body-swapping elements, along with MI training, significantly enhanced participants’ confidence to lose weight and readiness to perform physical exercise. In addition, this intervention proved effective in reducing dysfunctional eating behaviors and anxiety compared to the other groups. Overall, these findings are consistent with previous studies on the beneficial effects of positive, instructional, and motivational self-talk for performance [ 47 ].

The adherence analyses revealed that participants who had higher adherence to the treatment were those who reported, at T0, higher CR in relation to their eating habits, had healthier eating habits, and were in the maintenance stage of their change process, indicating a sustained commitment to lifestyle changes [ 8 ]. In line with these findings, a previous study showed that patients with obesity before BS who were more ready to limit food intake and were actively engaged in physical activity were more likely to adhere to dietary and physical activity recommendations after BS [ 48 ]. The dropout ratio of the study was 33.8% and was similar among the groups. This finding is aligned with another RCT that used VR psychological treatments for weight management [ 49 ], in which no differences between treatment conditions were found in the dropout rates.

As regards the sociodemographic information at T0, the average age closely aligns with findings from various studies involving patients who were on the BS waiting list or who had recently undergone surgery [ 50 ], as well as studies involving VR in the psychological management of obesity [ 51 ]. In addition, the uneven sex distribution in the study sample—79% (54/68) of the participants were female individuals—is common in studies with patients who have undergone BS [ 50 , 52 , 53 ], and also in VR interventions for weight management [ 49 , 51 ]. In fact, a recent systematic review that included 24 studies that used VR to treat obesity [ 15 ] reported that in 8 trials with both men and women, 93% of the sample were women. Several studies have explored this phenomenon [ 54 , 55 ], showing that women tend to experience higher body image dissatisfaction, psychological disturbances, and a greater desire to lose weight than men. This may explain why they are more frequently represented in clinical research studies.

Regarding clinical variables, consistent with previous literature [ 56 ], a high proportion of our sample (58/68, 85%) exhibited physical comorbidities, with pain, endocrine disorders, breathing problems, and cardiovascular problems being the most prevalent. These findings, together with the high prevalence of participants in our sample who were unemployed or on sick leave and with severe obesity (BMI: mean 43.58, SD 5.96 kg/m 2 ), further confirm the debilitating nature of the disease. In addition, the most prevalent mental disorders observed were anxiety (8/68, 12%) and depression (6/68, 9%). However, neither disorder reached significant levels of morbidity based on the recommended cutoff points of the HADS [ 57 ]. Interestingly, our results indicate lower levels of anxiety and depression compared to previous research on patients who have undergone BS [ 58 , 59 ].

Regarding the assessment of motivation to change, measured along 3 dimensions (importance, confidence, and readiness) over time, participants initially reported a high importance placed on losing weight or exercising more. This posed challenges in detecting significant changes over time, as they were predominantly situated in the action and maintenance phases, as indicated by the S-Weight scores. However, all 3 groups improved over time in confidence to exercise more and readiness to lose weight. This was likely influenced by their positive expectations related to the BS or their mere involvement in the study. The participants’ expectation of undergoing BS, in alignment with recent literature [ 53 ], potentially served as an additional motivator in their personal journey toward change [ 60 , 61 ]. Notably, most participants had already consulted with the nutritionist and endocrinologist of the obesity unit on multiple occasions and had received instructions on how to implement modifications to their diet and exercise routines in preparation for surgery. The adherence to these recommendations provided valuable insights into their likelihood of being eligible to undergo surgery soon. Consequently, the findings regarding changes in motivation to change over time remain inconclusive, making it difficult to draw specific conclusions regarding postintervention improvements in these aspects.

Regarding the group versus time effect on motivation to change, the results indicated that participants who used ConVRself reported a significant increase in their confidence to lose weight and a higher readiness to engage in exercise compared to the CG. These positive effects were maintained for a duration of between 1 and 4 weeks after the exposures. This finding is particularly encouraging for behavior change, given that, for a person to initiate and maintain a change process, they must believe that the change is important and have confidence in their ability to achieve it [ 8 ]. Notably, increased confidence is often associated with a greater propensity to adopt self-regulation skills, including control over eating behaviors and improvement in physical activity [ 62 ]. In this sense, our results derived from the TFEQ-R18 suggested improved control over eating behaviors over time among the ConVRself group, as evidenced by lower tendencies in EE and UE behaviors, compared to the CG. Previous studies employing VR interventions with people living with obesity have yielded similar results, consistently demonstrating increased self-efficacy and readiness to initiate behavior change [ 63 - 65 ].

Apart from the aforementioned improvement in eating control in EG1, the same group also experienced a notable reduction in anxiety from T0 to T1 compared to EG2 and CG. These findings align with the results of 2 RCTs conducted by Manzoni et al [ 64 , 65 ]. In these studies, it was demonstrated that a relaxation treatment augmented by VR was more effective in reducing anxiety and EE behaviors than traditional meditation interventions at 2-week and 3-month follow-ups among women living with obesity. It is worth mentioning that anxiety scores in EG1 decreased more after the intervention compared to EG2 and CG; however, this reduction was not sustained over time. We believe that this result can be attributed to the absence of ongoing psychological support after the intervention period with ConVRself. This finding emphasizes how patients can be highly sensitive to changes in the treatment process during the preoperative period and underscores the constant need for psychological support in all pre- and postoperative treatment phases [ 5 ]. In this context, similar to the successful application of VR in addressing various mental disorders, including anxiety [ 66 ], ConVRself has the potential to provide an additional benefit by enabling patients to cope with anxiety and develop strategies aligned with their values.

In line with our previous usability study [ 27 ], high SEQ scores on ConVRself indicated high usability and acceptance of the platform by people living with obesity, which means that the platform was well adapted to this population. In addition, as regards the body ownership of the avatars, the results obtained are similar to those reported in previous literature [ 25 , 27 ], which indicates that participants experienced a strong sense of body ownership over the virtual avatars. However, inconsistent outcomes were found with EG2 for the looking down at the virtual body item compared to the other body ownership evaluations. The likely reason for this anomaly is a technological problem. Whenever participants looked down while embodied in their look-alike avatars, they could only see their knees. This limited visibility of their legs was primarily due to their anatomy (body size and shape), which caused most of their legs to be out of view. EG2 participants, particularly, failed to infer that seeing their knees implied seeing their entire legs. Despite the avatar’s anatomy being the same for all participants, we believe that EG1 participants were more focused on engaging in self-conversation, while EG2 participants placed greater emphasis on the physical appearance of their avatars.

Limitations and Strengths

This study has several limitations. First, the high presence of physical comorbidities in the participants may have been influenced by the impact of delayed medical care and the exacerbation of conditions due to the COVID-19 pandemic [ 56 ]. This could potentially affect the generalizability of our results to the broader population. Second, a high dropout rate was observed, which made it challenging to achieve the expected adherence rate as stated in the study protocol [ 26 ]. This high dropout rate and the resulting smaller sample size may have impeded the detection of medium or small effects within the sample, particularly the potential differences among the groups. Third, the study design did not allow for a clear separation of the effects of ConVRself and the motivational training on the primary and secondary variables. The interpretations derived from our results could be attributed to either the effects of the motivational training or the virtual self-conversations, or, more likely, a combination of both. Fourth, the short follow-up period may limit the generalizability of the results. It becomes necessary to conduct a longer-term follow-up of the patients to observe whether the changes obtained with the ConVRself platform are sustained. Unfortunately, this was not feasible in this study due to time constraints imposed by the European SOCRATES project and the specific characteristics of our sample (patients on the BS waiting list). Furthermore, despite intensive basic training in MI, real competence in it demands constant and prolonged practice, potentially influencing the results. Finally, the high expectation of improvement from BS may have influenced the positive outcomes; therefore, it would be necessary to corroborate the results in a population with morbid obesity but without BS expectations.

The strengths of this study are its experimental design, specifically a study with 3 experimental groups and 4 assessment points, and the well-balanced distribution of the sample across groups. Moreover, we conducted a comprehensive evaluation of the participants’ health, including a clinical interview that considered both physical comorbidities and mental illness at T0. Furthermore, EG1 participants received intensive training before the VR intervention, which was led by an MI expert. In terms of statistical analysis, HLMs were used in conjunction with ITT analyses, enabling the inclusion of all available data from the study, including information from participants who dropped out. Finally, there are no previous studies on MI training aimed at patients rather than therapists, either in obesity or other medical fields. This opens up possibilities to train patients as experts, making them self-aware about their own condition and capable of self-motivation.

Conclusions

In conclusion, using VR self-conversations to address the root causes of obesity has demonstrated important benefits and can be safely applied, with no side effects, among this population. In particular, the VR self-conversation with novel techniques of embodiment and body-swapping was well received by EG1 participants and was effective in enhancing self-efficacy and readiness to change, as well as in reducing dysfunctional eating behaviors and anxiety, compared to the other groups. Despite the apparent complexity of the procedures (self-conversation with embodiment and body-swapping), participants were able to complete the exposures, and they engaged in meaningful self-conversations about their obesity-related challenges and potential solutions. In this regard, a future study will provide qualitative data (currently under analysis and subject to another publication) on the unfolding of the motivational self-conversation process.

As for future perspectives, our findings underscore the importance of incorporating innovative psychological interventions to promote overall well-being and facilitate improvements in eating behaviors and lifestyle beyond mere weight loss. Such integrated interventions are crucial not only during the preoperative phase but also for the long-term maintenance of positive outcomes after BS. Future research should be conducted with ConVRself as a treatment not only for people living with obesity but also for patients with mental disorders or addictive behaviors. The potential of enriching virtual self-conversation during moments of blockage in patients with artificial intelligence techniques presents an exciting future research line.

Acknowledgments

The authors would like to thank the rest of the SOCRATES consortium (Self Conversation in Virtual Reality Embodiment to Enhance Healthier Lifestyles Among People with Obesity) for their contributions to the project. In addition, they would like to thank the staff of the obesity unit and psychiatry department at the Vall d’Hebron University Hospital for their help during the recruitment process. Finally, the authors would like to thank all participants who contributed to this study. This study was funded by the European Union’s Horizon 2020 Research and Innovation Programme (951930). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This study was also financially supported by the Serra Húnter Programme in the form of a grant awarded to DA.

Data Availability

The data sets generated and analyzed during this study are available from the corresponding author on reasonable request.

Authors' Contributions

DA, MS, JARQ, and PLP conceived the study. DA, along with PH and PGR, wrote the manuscript. DA, JVDS, PH, and PGR conducted the recruitment during data collection. PLP led the motivational interviewing training for experimental group 1 participants. PGR conducted all statistical analyses and, with the support of PH, created all tables and figures for the study. JVDS and PLP provided valuable revisions during the manuscript writing process. BS, EAdlC, and MS adapted the virtual reality platform to the needs of people living with obesity and offered technical support during the study. AC provided support during the initial phases of the study, and MC contributed to the sample collection. Finally, BS and MS provided valuable feedback regarding previous revisions of the paper.

Conflicts of Interest

MS and BS are founders of Virtual Bodyworks SL, a spin-off company of the Universitat de Barcelona. EAdlC was employed by Virtual Bodyworks SL. All other authors declare no other conflicts of interest.

Procedure and timeline of the randomized controlled trial.

Sociodemographic and clinical characteristics of all participants, and separately for each group.

Descriptive statistics (mean and SD) on scales and subscales of the study divided into groups and time measures.

Stages of Change Questionnaires for Weight Management frequencies and percentages divided into time and groups.

Estimated means of secondary outcomes in group versus time effect (intent-to-treat analysis).

CONSORT checklist.

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Abbreviations

Edited by A Mavragani; submitted 03.08.23; peer-reviewed by N Farič, V Girishan Prabhu; comments to author 22.12.23; revised version received 12.01.24; accepted 30.01.24; published 05.04.24.

©Dimitra Anastasiadou, Pol Herrero, Paula Garcia-Royo, Julia Vázquez-De Sebastián, Mel Slater, Bernhard Spanlang, Elena Álvarez de la Campa, Andreea Ciudin, Marta Comas, Josep Antoni Ramos-Quiroga, Pilar Lusilla-Palacios. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Obesity Research Paper

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Obesity has increasingly been identified as a critical global public health concern. This focus on obesity as a health priority raises complex bioethical issues. These include how obesity is defined and categorized, the implications of the centrality of personal responsibility in medical and public health approaches, how competing ethical frames impact social justice concerns, and the growing “moral panic” concerning obesity. A critical examination of how obesity is defined as a medical problem suggests that ethical approaches could be more productive if obesity were addressed as a social problem with medical consequences, rather than emphasizing it as a medical problem with social consequences.

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There has been a dramatic rise in the prevalence of obesity globally in the last three decades, and the World Health Organization (WHO) estimates around 11 % of the world’s total population is obese (WHO 2012). Obesity is seen as a major public health concern because it is widely recognized as a precipitating factor in the parallel emergence of chronic diseases as a primary cause of death in many countries. Obesity is often reported as a major drain on medical systems, and the growing obesity rates in developing countries are often cited as especially worrying in this regard. From a bioethics perspective, the focus on obesity as a health priority raises complex issues. This entry highlights inter-related and key bioethical dimensions of contemporary concerns around and approaches to obesity, including the means by which people are categorized as obese or not, the medicalization of obesity as a disease that needs to be treated, implications of the centrality of individual responsibility in medical and public health approaches, obesity as a social justice issue, and media and growing “moral panic.”

Obesity is most simply defined as an excess of adipose (fat) tissue, usually with negative health effects. However, this definition is problematic. Medically, as discussed below, the science of obesity is increasingly suggesting that many people can be both obese and healthy. However, “obese” and “obesity” are terms that have also entered everyday media and other public discourses in ways that are mostly negative and imply ill-health and disease. The growing assumption that obesity is defined as a negative characteristic is historically and culturally particular, in marked contrast to cross-cultural records that describe plump bodies as powerful, sexy, social, abundant, fertile, and certainly healthy (Brewis 2011a).

Ethical Dimensions

The Categorization of Obesity. A definition of obesity based upon the notion of excess body fat requires measurement against a standard of what constitutes “normal.” Given that human bodies are highly ecologically flexible and vary in averages across populations, the imposition of a single standard for classification as obese raises some complex bioethical issues. The most widely employed means to classify people as obese, and then assess variation in population levels of obesity, is through use of body mass index (BMI).

BMI does not directly measure body fat; rather, it is a proxy measure using the ratio of mass (weight) relative to height. Using statistical methods and prescriptive and risk models, four basic categories of weight (underweight, normal, overweight, obese) have been identified and are now widely applied, from the doctor’s office to large public health interventions. These standard categories are arbitrarily defined through cutoff points related to morbidity and mortality rates found in large-scale epidemiological studies, with obesity normally set at a BMI of 30 or higher.

While BMI as a measure of obesity is sometimes useful, particularly in clinical studies, because of both individual and population variation, this mapping of weight to health risk is not precise or even especially predictive. For example, there is growing evidence that many people clinically defined as obese prove to be metabolically healthy even as they are advised by doctors they need to lose weight, and that the level of obesity at which conditions like diabetes and heart disease become more prevalent differs across populations. Moreover, BMI does not discriminate between muscle mass, bone, connective tissue, and amount types of adipose tissue, obscuring accurate measurement of total body fat. As a result, people with highly-developed musculature are labeled obese by the measure, even when they have low levels of actual body fat. Further, some populations have greater bone density on average or shorter leg bone length resulting in falsely high BMI scores (Hruschka et al. 2013). For example, for decades there has been a public health concern focused on very high obesity risk in Pacific Island populations, but more recent studies have shown that the disease correlates of obesity emerge at higher levels of adiposity in comparison to other groups. Hence, the common standard for categorizing obesity probably misassigns a significant number of people and accordingly implies health risks where none may exist (and vice versa). Additionally, women have a higher percentage of body fat than men, and weight tends to increase in both genders as individuals age. Attempts to address the weaknesses in BMI classifications have resulted in alternative methods that more accurately measure the amount and distribution of body fat, but these use technologies or expertise that are difficult to implement in real-world settings.

Defining Obesity as a Disease. Defining obesity against a set standard of what is a normal or healthy level of body fat leads to an emphasis on prevention and cure, and underscores obesity as (1) a problem, with (2) an identifiable cause (diagnosis), and that (3) requires evaluation, intervention, management, and control. The central bioethical issue is this: regardless of how people are classified into an obese category, once so categorized it is generally assumed that labeling a person as unhealthy is warranted and medical or other intervention is necessary. Certainly, obesity has become increasingly identified as a major factor and index of ill-health over the last two decades. This culminated in the formal recognition of obesity as a disease by the American Medical Association in 2013, even in the absence of other risk factors or clinical symptoms. The growing medicalization of obesity as a condition explains why highly invasive and often risky medical treatments for obesity, such as bariatric surgery, are on the rise. The emphasis on excess weight as a health problem also negatively impacts how people view and relate to their own and others’ bodies and in ways that create emotional and social distress related to failing to meet social prescriptions for an ideal or acceptable body size.

Levels of Analyses and Ultimate Causation. Current scientific evidence on the causes of obesity can be analyzed at different levels, often working iteratively and in feedback with each other. At the genetic level, some individuals have a predisposition toward higher weights, weight gain, and difficulty in weight loss, related to genetic variants in appetite, metabolism, and activity. At the individual level, obesity is the result of excess calorie intake over calories expended through physical activity, but individual-level factors such as income, education level, ethnicity, age, and gender also predict differential risks of being obese, as does use of certain medications or comorbidities such as depression. Institutional factors such as health care access also matter.

At the community, neighborhood, or regional level, obesity risk accrues differently based solely on where people live. One factor in this pattern is the rapid urbanization of the world’s population: urbanization is associated with higher rates of obesity, and an increasing majority of humans live in cities. This correlation is due, in part, to the low cost of high density foods, changes in activity with the move to urban settings and structural and economic barriers to healthier lifestyles (Metzl and Hansen 2014). Further, within those cities, specific locales and their inhabitants’ lifestyles vary based upon social, spatial, and economic factors. The built environment of a particular locale is one example of how the physical expression of social, spatial, and economic factors relates to obesity prevalence: walkability, public transportation, access to fresh foods, safety, parks, light and shade, access to healthcare, and density all help shape obesity risk. For example, barriers in transportation and distance may make it difficult for residents to access healthy foods, while the perception by residents that the place they live in is unsafe or of poor quality may limit opportunities to be physically active. Social and economic factors also influence residential effects, including social exclusion, discrimination, and diminished economic infrastructure. Efforts to address residential effects often evoke stakeholder objections, as these efforts may inhibit personal choice, stigmatize neighborhood residents, or create changes that conflict with personal lifestyles and cultural values (ten Have et al. 2011).

Education and wealth, and most especially poverty, are also implicated in obesity risk. The relationship between income and obesity is complex and varies depending on the economic development of the resident country. Most nations, even the poorest, demonstrate some level of obesity, even in the presence of food shortages and undernutrition. The combination of under and over nutrition increases the likelihood of obesity and has significant implications in terms of health risks and negative health effects. As poorer nations become increasingly urbanized and industrialized, these problems are exacerbated, particularly as low income countries have fewer healthcare resources to meet the challenges posed by chronic conditions associated with obesity. This “dual burden” is also evident in middle-income countries: as economic changes at both the household and national level occur, families with a dual burden of having overweight and underweight individuals become increasingly prevalent.

Evidence suggests that income and obesity also rise together as inexpensive food becomes easily accessible. However, this trend reverses at the point where the apparent social costs of obesity outweigh the advantages. In middle to high-income countries, obesity tends to be inversely correlated with socioeconomic status, meaning that the highest obesity rates are found in those populations with the lowest incomes and with the lowest levels of educational achievement (Brewis 2011a). At a national level, BMI appears to rise in the early and accelerated phases of economic development due to a complex set of factors including urban migration, a shift from traditional occupations, and increased technology. At the individual level, poverty is contextual, demonstrating a complex residential pattern, with both rural and urban poverty linked to lower education and higher obesity.

While there have been some efforts to develop community-level interventions in line with increasing recognition of these upstream causes of obesity risk, medical and public health interventions continue to give the most attention to individual behavior change. The standard treatment model, often shared by clinicians and patients alike, is that the individual must lose excess weight by eating less and/or exercising more. This is despite decades of evidence that most such behavioral change strategies eventually fail to result in weight lost, and often serve to promote weight regain (Brewis 2011a).

Obesity and Social Justice Considerations. The role of proximate and ultimate factors discussed above means that obesity can be framed as a social justice issue, not solely a medical one. This suggests a very different course, emphasis, and pathway for public health interventions. Policies that seek to restrict behavior (passively or actively) can disproportionately affect the poor, the rural, and the malnourished. Of critical importance is who designs, implements, and evaluates these efforts. How do these interventions ethically impact personal physical health while promoting equality and maintaining individual autonomy? If population-level interventions are not necessarily individually beneficial and may in fact have psychosocial and cultural costs with their own negative health consequences, should public health entities intervene at all? These are some of the ethical issues that arise when the focus moves away from considering obesity fundamentally a medical problem to thinking about obesity at the aggregate level.

The challenge is to consider both the ultimate (structural) as well as the proximate factors (nutrition, activity, and medical conditions) that shape obesity risk when developing obesity policy and interventions. Identifying the causes of obesity, when coupled with how it is defined, becomes important in the ethical frame used to intervene. To date, there have been multiple framings in approaches to combat the rise of obesity. These ethical frames are not mutually exclusive and often coexist within a particular approach. Understanding the ethical platform from which programs spring will enable better understanding of the consequences (intentional or unintentional), successes, and failures. Identifying obesity as a health problem is more than defining disease, biomedical risk, and treatment; assigning responsibility – individual or otherwise – becomes part of the equation. The increasing prevalence of obesity on a global scale is accompanied by concerns that society is harmed in some way. This sense of harm in turn is linked to the notion of blame. How responsibility and blame are assigned varies with different ethical frames.

Framing Obesity Solutions

Emphasis on Individual Responsibility. The notion of individual responsibility has dominated the discourse surrounding the obesity crisis and efforts to contain the problem. Individual responsibility is rooted in notions of individual autonomy based within a moralistic theory of personal determination. Morality frames emphasize the threat to social values and economic stability by focusing on personal choice and the impact these choices have on society (Boero 2012). A morality frame advances notions of normal, ideal, virtue, right, and wrong. In this frame, obesity is related to personal failings – a lack of self-discipline, restraint, rationality, and moral failings attributed to poor life choices (gluttony, sloth, and a lack of adherence to personal improvement). Obesity, therefore, is self-induced and harm is self-inflicted. Because the individual is responsible for their health and body, blame is personal and can take the form of value imperatives about who is obese or overweight and who is responsible. Interventions and public health campaigns using this frame focus on problem awareness, promote better individual health behaviors, and encourage personal responsibility. Interventions range from educational efforts to weight loss programs, “fat taxes” (on calorie or fat dense foods), and increased insurance rates for individuals with high BMIs. This type of framing, when used in conjunction with a medical definition of obesity, places the focus of the intervention on achieving a physical ideal body weight and ignores the psychosocial dimensions of health, even as it places responsibility upon the individual (as psychologically weak or morally lax). Stigmatization, discrimination, and negative self-image are the result, which have their own negative health consequences (Sagay 2013; Puhl and Heuer 2010).

Biomedical and Public Health Frames. The biomedical frame uses the language of risk to intervene and regulate the body in order to promote health or, more usually, decrease illness or disease. Obesity in this frame is seen as pathologic – a biological condition to be monitored, treated, and cured. The body is understood to be the recipient of treatment, a somewhat passive vessel that needs management by healthcare professionals (Sagay 2013). De-emphasizing personal responsibility can be helpful in decreasing stigma, but medicalization also promotes stigmatization by labeling obese bodies as sick. Framing obesity in terms of mortality and morbidity imparts urgency and authority to the issue. The locus for intervention is on proximate factors and responsibility remains with the individual-aspatient, though the medical system is a crucial partner in terms of defining the problem and determining and managing treatment. Generally individual and small-scale interventions focused on dietary choice, activity, and medical/surgical interventions are utilized in this context. However, the biomedical frame informs larger policy issues resulting in industry and governmental regulations generally rooted in economic analyses, such as differential insurance rates for individuals based upon weight, corporate programs to incentivize weight reduction or dietary choice, bans or taxes on sugar-sweetened beverages, and regulation of nutritional information on food products.

A public health frame assigns responsibility to the government (local, state, and federal). Public health entities are most often located within governments and are charged with setting standards, regulating and protecting public safety and promoting health, and minimizing or preventing public harm while at the same time ensuring individual liberty, privacy, and public access to needed resources. This equation differs internationally as notions of individual and public health are culturally constituted. In general, obesity is seen as a threat to public health and the approach taken is to reduce the threat, generally combining individual and systemic approaches to address the issue. Ethical approaches in this frame deal with the differential distribution of obesity across groups and subpopulations as prevalence and risk manifest variably within cultural groups, gender, socioeconomic status, etc. Financial triggers (incentives & disincentives), built environment changes that alter lifestyle options (slowing elevators, car-free zoning, food banning), and informational campaigns are often used or suggested within a public health intervention. Issues of justice and fairness can be particularly problematic in this framing as the dual focus of public health creates a tension between liberty and protection. Obesity at the individual level includes social and economic disparities as well as discrimination and psychological stress from weight bias. Addressing these issues within the systemic frames of government, business, and infrastructure (including larger social forces) can contribute to stigmatization, discrimination, and differential opportunities and access.

Thus, in practice, there is a smorgasbord of antiobesity efforts, structured within multiple framings – moralistic, biomedical, and public health – that tend to be disconnected from each other. Even assuming a universal definition of obesity and its determinants exists, the ethics of policy interventions still needs to be addressed. At the heart of the ethics, debate is concerned over individual choice, autonomy, and the exacerbation of stigma and discrimination. Rephrasing the two previous ethical questions might then ask: What are the individual’s essential rights and responsibilities concerning weight? Secondly, what is the responsibility of the government in providing healthy, safe environments for its citizens?

This tension between rights and responsibilities (individual, societal, and governmental) plays out differently globally. The body (and body size) is understood as a “domain of liberty and autonomy” (Tirosh 2014, p. 1801), but the expression of these values is differentially understood across societies. When seen as a lifestyle issue, obesity remains focused at the individual and local levels, to be dealt with through small-scale interventions in select populations to encourage individuals to control their weight and make healthier choices (moralistic frame). These types of interventions tend to ignore the complexity of factors (and responsibilities) underlying obesity and keep responsibility (and blame) with the individual. Growing public discourse has revolved around policy changes to combat the “rising epidemic” of obesity. Public health officials have supported this groundswell of opinion through campaigns to promote the adoption of a healthy lifestyle, emphasizing a diet high in fruits, vegetables, complex carbohydrates, and lean proteins and sufficient exercise – efforts that highlight personal choice and responsibility. Much of the work on prevention and intervention at this level has had mixed results. Even among public health practitioners who seek to address structural components underlying obesity, the political weight of the morality frame leads them to use “code language” such as “make the healthy choice, the easy choice.” Essentially structural changes are presented as changes enabling personal choice.

At a governmental level, rising healthcare costs in conjunction with rising obesity rates globally and concerns over the efficacy of individual-level interventions are frequently cited as an impetus for governmental strategies and policies to guide widespread interventions, primarily through legislation. Governmental interventions are influenced by the culture, political system, economics, and traditions of the nations involved, resulting in a spectrum of policies and programs globally. Efforts range from health education to restrictive taxes on unhealthy foods and beverages, with a goal of shaping behavior by restricting or coercing individual choice. In the European Union (EU), a concerted effort is being made to encourage voluntary action on the part of industry partners to alter nutrition and activity environments. Voluntary efforts to support decision-making through evidence-based information, self-regulation of product claims (labeling, advertisements) through the proposed establishment of an industry code of conduct, food redistribution (surplus fruits/vegetables) focused on children 4–12 years old, reformulation of foods to decrease sugar, fat, and salt, and sustainable urban transportation facilities to promote physical activity/ public infrastructure (Commission of the European Communities 2007) are examples of this type of intervention. In the USA, taxation of SSBs and calorie-dense foods has been implemented (or attempted), most notably in New York City and the Navajo Nation. China, Britain, and Mexico have all passed or attempted to enact legislation that aims to regulate behavior with an eye to reducing the economic burden of healthcare. Often, particular populations are targeted for interventions, as evidence indicates that obesity is more prevalent in these groups. Unfortunately, these efforts can take the form of value imperatives about who is obese or overweight and who is responsible, encouraging the spread of stigmatization and victimization (Puhl and Heuer 2010).

Some initiatives have sought to create structural or environmental changes to address the inequities, disparities, and deficits implicated in obesity (public health framing with social justice focus). Policies attempting to reduce the unequal distribution of resources, barriers to healthy foods and activities, and social and economic inequities can be found in new regulations requiring enhanced visibility and simplified nutritional labeling; limitations on commercial advertising of high density, low-nutrient foods to children; venue-specific banning of “unhealthy items” such as high-fat items in restaurants or SSBs in school vending machines; and limiting the proximity of fast-food restaurants to schools (Kass et al. 2014; ten Have et al. 2011). These types of initiatives still impact personal choice and liberty and have resulted in public debates regarding the role of government in regulating health. Impacting broader economic and social structures is more challenging from the local level, though increasingly tools like health impact assessments and health in all policies are being used to provide more equity in land use decisions, and have even been used to evaluate local minimum wage, affordable housing, and supplemental nutrition policies. Criticisms of obesity policies have ranged from concerns over the inhibition of individual autonomy, the expansion of the paternalistic “nanny” state (and subsequent economic burden), and the inequitable treatment and stigmatization of low-income populations.

Ethical discussions concerning interventions that limit choice or coerce behavior tend to be centered on arguments about legitimacy and utility. Legitimacy focuses on the value to society in instituting a particular policy or practice. Generally, the discussion revolves around the role of paternalism (soft or hard) in promoting the general welfare of the individual. Paternalism is best viewed as a sliding scale that ranges from promoting informed choice (information campaigns) through implementation of incentives (free or reduced costs, tax benefits, etc.) and ultimately various forms of coercion (bans, taxation). Utility looks at the cost-benefit ratio: is a policy or intervention likely to succeed and does it offer enough benefit to offset the reduction in choice, liberty, or privacy. Because there is little cohesion in how data is collected internationally, making evidence based comparisons of the effectiveness of different types of interventions is difficult. In general, arguments made for coercive policies are rooted in the premise that obesity is associated with higher morbidity and attendant higher costs of treatment. As previously noted, this is by no means a validated conclusion and therefore the utility of such efforts is suspect.

An example of this trade-off is the call for school districts to restrict soft drinks on school campuses. This type of intervention may have the unintended consequence of reducing the school’s revenue stream, resulting in less money available for student education or extra-curricular activities. Obesity prevalence is associated with poverty and disadvantage, disproportionately impacting precisely those communities whose schools need funding the most. Reduced funding may lead to a reduction in programming and healthy food options, elimination of physical education or play equipment, poor food quality to reduce costs, increased sedentism, and reduced educational opportunities (Crooks 2003). The result may be an environmental trade-off of biological costs for social benefits – poorer nutritional quality in order to provide education for all students and thus hopefully propel the students out of poverty.

Another example is the call to use social pressure tactics, similar to antismoking campaigns, to leverage public opinion toward acceptance of stringent governmental regulations. The trade-off here is to focus on increased legitimacy at the expense of utility. This type of intervention operates at the individual, acute, and proximate level and does not address any of the underlying structural conditions. Couched as “stigmatization lite” the argument is that overweight and obese individuals do not recognize their “problem” and need to be awakened to reality. Unfortunately increasing stigmatization of the individual has not been demonstrated to positively impact behavior change; rather, it produces the opposite impact. Discrimination is implicated in stress induced physiological responses associated with obesity that not only negatively impact health but also discourage potential participation in health-related activities. Beyond this, how is the level of stigma “titrated?” Increasing antiobesity thinking may contribute to the moral panic over the rise in obesity rates (Campos et al. 2006).

Stigmatization And Moral Panic

Obesity and Weight-related Stigma. Any discussion on bioethics needs to address the issue of stigmatization (and resulting victimization and discrimination) of obese individuals. Placing the responsibility for one’s weight on the individual has led to sanctioned discrimination in the form of diminished access to goods, services, and employment opportunities and higher healthcare costs for obese individuals. Obesity has even been used as evidence in child abuse cases and other legal interventions. Despite multiple framings of obesity as a medical and public health problem, the persistent focus on individual responsibility and autonomy continues to direct the understanding of obesity through the lens of morality – a platform for value imperatives and subsequent stigmatization.

Obesity stigma must be addressed within the social and structural conditions that produce it. That is, there must be recognition that even a focus on ultimate factors (zoning laws, bans, taxation, urban renewal) can have unintended consequences resulting in increased discrimination. In the past, public health concerns were often the result of an external agent (bacterial or viral agent, poor sanitation, cigarettes, etc.), allowing the focus of interventions to remain external to the body/self. However, weight (and excess weight) is rooted in the body itself – it is a domain of the self. Eating and movement are necessary components of life and are seen as highly personal, as one chooses what, when, and how to eat, move, and function bodily within personal environments. Because these activities are necessary (one cannot stop eating, for example), efforts have focused on changing personal decisions related to eating and activity. Attempts to alter these bodily functions with an external agent (medication, surgery) have had mixed results, but as long as eating and activity are categorized as personal choices, stigmatization will remain a factor.

Media and Corporate Roles. The “moral panic” that has resulted from the framing of obesity as an epidemic has produced a media onslaught. This begs the question of whether the media is reflecting this panic or creating it. Popular media promotes a thin ideal body size (particularly for women), while continuing to also promote the sale of obesogenic products. Fast food and junk food advertisements, product placement in movies, casting of thin ideal body types, and disparaging characterizations of obese characters are prevalent throughout multiple media formats. Visual representations of obese bodies that employ “de-evolution tropes” (which portray the human species as degenerating from more fit ancestors) are common. Media use (screen time) is certainly associated with increased snacking and requests for caloriedense foods and decreased activity and altered sleep patterns (American Academy of Pediatrics 2011).

The increasing documentation of these negative social and physical impacts of media treatment of obesity has led to a mishmash of corporate efforts and legislative calls to action. For example, the Disney Corporation has announced that it will no longer advertise “junk foods” on its television channel. However, Disney continues to promote thin body ideals in its movie and cartoon heroines. McDonald’s has been criticized for targeting children with “toy” gifts in their high fat and sugar Happy Meals. Several European Union countries have instituted restrictions on food advertising aimed at very young children. The impacts of the media on obesity risk and stigma bring to the fore the ongoing ethical conundrum concerning the extent to which governments should have control over media that promote unhealthy behaviors or stigmatization. Issues of free speech, government regulation, and equal access to opportunity and goods have all been cited as deterrents to government regulation of advertising and media. Combining this with a moralistic frame that castigates large bodies as personal failures and the bioethical landscape is messy indeed.

Obesity arises through individual behaviors shaped within varied epigenetic, cognitive, sociocultural, physical, material, political, and other institutional structures and environments. Bioethically, based on the discussion above, this entry suggests that obesity is perhaps more productively addressed as a social problem with medical consequences rather than a medical problem with social consequences. Competing frames of obesity, whether medically or otherwise problematized or not (moralistic, medical/ healthcare, public health, governmental), are rooted in concerns about the ethical behavior of members within the group, not about the larger social, economic, and political domains. Social justice models for obesity intervention rightly focus on the role of the built environment, but rarely tackle the ultimate determinants like poverty, education, and discrimination. Many complex bioethical questions remain: Is it possible to account for acute and chronic dimensions as well as proximate and ultimate factors and mitigate some of the unintended, negative consequences of interventions? How can health policies and interventions ethical approaches be constructed to take into account the very real social dimensions of weight and the body? If health is a public good, what are the ethical implications of not intervening?

Ultimately, being obese is both a private and public matter. While an individual’s weight is the result of multiple individual and biosocial components, the individual’s body is subject to public scrutiny and – increasingly – public regulation. The consequences of public efforts, both intended and unintended, need to be critically examined within the context of how obesity is defined as a problem, the frame used to address the problem as defined, and then how, with whom, and at what level various prevention and intervention efforts are implemented.

Bibliography :

• American Academy of Pediatrics. (2011). Policy statement – children, adolescents, obesity, and the media. Pediatrics, 128(1), 201–208.
• Boero, N. (2012). Killer fat: Media, medicine, and morals in the American “Obesity Epidemic.” New Brunswick: Rutgers University Press.
• Brewis, A. (2011a). Obesity: Cultural and biocultural perspectives. New Brunswick: Rutgers University Press.
• Campos, P., Saguy, A., Ernsberger, P., Oliver, E., & Gaesser, G. (2006). The epidemiology of overweight and obesity: Public health crisis or moral panic? International Journal of Epidemiology, 35, 55–60.
• Commission of the European Communities. (2007). A strategy for Europe on nutrition, overweight and obesity related health issues. Brussels, SEC. 706–7.
• Crooks, D. (2003). Nutrition and the sale of snack foods. Medical Anthropology Quarterly, 17(2), 182–199.
• Hruschka, D. J., Rush, E., & Brewis, A. (2013). Population differences in the relationship between height, weight, and adiposity: An application of Burton’s model. American Journal of Physical Anthropology, 151, 68–76.
• Kass, N., Hecht, K., Paul, A., & Birnbach, K. (2014). Ethics and obesity prevention: Ethical considerations in 3 approaches to reducing consumption of sugar-sweetened beverages. Public Health Ethics, 104(5), 787–795.
• Metzl, J., & Hansen, H. (2014). Structural competency: Theorizing a new medical engagement with stigma and inequality. Social Science & Medicine, 103, 126–133.
• Puhl, R., & Heuer, C. (2010). Obesity stigma: Important considerations for public health. Framing Health Matters, 100(6), 1019–1028.
• Sagay, A. (2013). What’s wrong with fat? Oxford: Oxford University Press.
• ten Have, M., de Beaufort, I. D., Teixera, P. J., Mackenbach, J. P., & van der Heide, A. (2011). Ethics and prevention of overweight and obesity: An inventory. Obesity Reviews, 12, 669–679.
• Tirosh, Y. (2014). Three comments on paternalism in public health. Connecticut Law Review, 46(5), 1795–1816.
• World Health Organization. (2012). Obesity and overweight: fact sheets. World Health Organization. Retrieved from https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight
• Brewis, A. (2011b). Obesity: Cultural and biocultural perspectives. New Brunswick: Rutgers University Press.
• Puhl, R. M., & Heuer, C. A. (2009). The stigma of obesity: A review and update. Obesity, 17(5), 941–964.
• Saguy, A. (2012). What’s wrong with fat? Oxford: Oxford University Press.

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Feingold KR, Anawalt B, Blackman MR, et al., editors. Endotext [Internet]. South Dartmouth (MA): MDText.com, Inc.; 2000-.

Endotext [Internet].

• www.endotext.org

Definitions, Classification, and Epidemiology of Obesity

Jonathan Q. Purnell , MD.

Last Update: May 4, 2023 .

Recent research has established the physiology of weight regulation, the pathophysiology that leads to unwanted weight gain with establishment of a higher body-weight set point, and the defense of the overweight and obese state even when reasonable attempts in lifestyle improvement are made. This knowledge has informed our approach to obesity as a chronic disease. The assessment of adiposity risk for the foreseeable future will continue to rely on cost-effective and easily available measures of height, weight, and waist circumference. This risk assessment then informs implementation of appropriate treatment plans and weight management goals. Within the United States, prevalence rates for generalized obesity (BMI > 30 kg/m 2 ), extreme obesity (BMI > 40 kg/m 2 ), and central obesity continue to rise in children and adults with peak obesity rates occurring in the 5 th -6 th decades. Women may have equal or greater obesity rates than men depending on race, but less central obesity than men. Obesity disproportionately affects people by race and ethnicity, with the highest prevalence rates reported in Black women and Hispanic men and women. Increasing obesity rates in youth (ages 2-19 years) are especially concerning. This trend will likely continue to fuel the global obesity epidemic for decades to come, worsening population health, creating infrastructural challenges as countries attempt to meet the additional health-care demands, and greatly increasing health-care expenditures world-wide. To meet this challenge, societal and economic innovations will be necessary that focus on strategies to prevent further increases in overweight and obesity rates. For complete coverage of all related areas of Endocrinology, please visit our on-line FREE web-text, WWW.ENDOTEXT.ORG .

• INTRODUCTION

Unwanted weight gain leading to overweight and obesity has become a significant driver of the global rise in chronic, non-communicable diseases and is itself now considered a chronic disease. Because of the psychological and social stigmata that accompany developing overweight and obesity, those affected by these conditions are also vulnerable to discrimination in their personal and work lives, low self-esteem, and depression ( 1 ). These medical and psychological sequelae of obesity contribute to a major share of health-care expenditures and generate additional economic costs through loss of worker productivity, increased disability, and premature loss of life ( 2 - 4 ).

The recognition that being overweight or having obesity is a chronic disease and not simply due to poor self-control or a lack of will power comes from the past 70 years of research that has been steadily gaining insight into the physiology that governs body weight (homeostatic mechanisms involved in sensing and adapting to changes in the body’s internal metabolism, food availability, and activity levels so as to maintain fat content and body weight stability), the pathophysiology that leads to unwanted weight gain maintenance, and the roles that excess weight and fat maldistribution (adiposity) play in contributing to diabetes, dyslipidemia, heart disease, non-alcoholic fatty liver disease, obstructive sleep apnea, and many other chronic diseases ( 5 , 6 ).

Expression of overweight and obesity results from an interaction between an individual’s genetic predisposition to weight gain and environmental influences. Gene discovery in the field of weight regulation and obesity has identified several major monogenic defects resulting in hyperphagia accompanied by severe and early-onset obesity ( 7 ) as well as many more minor genes with more variable impact on weight and fat distribution, including age-of-onset and severity. Several of these major obesity genes now have a specific medication approved to treat affected individuals ( 8 ). However, currently known major and minor genes explain only a small portion of body weight variations in the population ( 7 ). Environmental contributors to obesity have also been identified ( 9 ) but countering these will likely require initiatives that fall far outside of the discussions taking place in the office setting between patient and provider since they involve making major societal changes regarding food quality and availability, work-related and leisure-time activities, and social and health determinants including disparities in socio-economic status, race, and gender.

Novel discoveries in the fields of neuroendocrine ( 6 ) and gastrointestinal control ( 10 ) of appetite and energy expenditure have led to an emerging portfolio of medications that, when added to behavioral and lifestyle improvements, can help restore appetite control and allow modest weight loss maintenance ( 8 ). They have also led to novel mechanisms that help to explain the superior outcomes, both in terms of meaningful and sustained weight loss as well as improvements or resolution of co-morbid conditions, following metabolic-bariatric procedures such as laparoscopic sleeve gastrectomy and gastric bypass ( 11 , 12 ).

Subsequent chapters in this section of Endotext will delve more deeply into these determinants and scientific advances, providing a greater breadth of information regarding mechanisms, clinical manifestations, treatment options, and prevention strategies for those with overweight or obesity.

• DEFINITION OF OVERWEIGHT AND OBESITY

Overweight and obesity occur when excess fat accumulation (globally, regionally, and in organs as ectopic lipids) increases risk for adverse health outcomes . Like other chronic diseases, this definition does not require manifistation of an obesity-related complication, simply that the risk for one is increased. This allows for implementation of weight management strategies targeting treatment and prevention of these related conditions. It is important to point out that thresholds of excess adiposity can occur at different body weights and fat distributions depending on the person or population being referenced.

Ideally, an obesity classification system would be based on a practical measurement widely available to providers regardless of their setting, would accurately predict health risk (prognosis), and could be used to assign treatment stategies and goals. The most accurate measures of body fat adiposity such as underwater weighing, dual-energy x-ray absorptiometry (DEXA) scanning, computed tomograpy (CT), and magnetic resonance imaging (MRI) are impractical for use in everyday clinical encounters. Estimates of body fat, including body mass index (BMI, calculated by dividing the body weight in kilograms by height in meters squared) and waist circumference, have limitations compared to these imaging methods, but still provide relevant information and are easily obtained in a variety of practice settings.

It is worth pointing out two important caveats regarding cuurent thresholds used to diagnose overweight and obesity. The first is that although we favor the assignement of specific BMI cut-offs and increasing risk ( Table 1 ), relationships between body weight or fat distribution and conditions that impair health actually represent a continum. For example, increased risk for type 2 diabetes and premature mortality occur well below a BMI of 30 kg/m 2 (the threshold to define obesity in populations of European extraction) ( 13 ). It is in these earlier stages that preventative strategies to limit further weight gain and/or allow weight loss will have their greatest health benefits. The second is that historic relationships between increasing BMI thresholds and the precense and severity of co-morbidities have been disrupted as better treatments for obesity-complications become available. For example, in the past several decades, atherosclerotic cardiovascular (ASCVD) mortality has steadily declined in the US population ( 14 ) even as obesity rates have risen (see below). Although it is generally accepted that this decline in ASCVD deaths is due to better care outside the hospital during a coronary event (e.g., better coordination of “first responders” services such as ambulances and more widespread use by the public of cardiopulmonary resusitation and defibrillator units), advances in intensive care, smoking cessation, and in the office (increased use of aspirin, statins, PCSK9 inhibitors, and blood pressure medications) ( 15 ), these data have also been cited to support the claim that being overweight might actually protect against heart disease ( 16 ). In this regard, updated epidemiological data on the health outcomes related to being overweight or having obesity should include not just data on morbidity and mortality, but also health care metrics such as utilization and costs, medications used, and the number of treatment-related procedures performed.

• CLASSIFICATION OF OVERWEIGHT, OBESITY, AND CENTRAL OBESITY

Fat Mass and Percent Body Fat

Fat mass can be directly measured by one of several imaging modalities, including DEXA, CT, and MRI, but these systems are impractical and cost prohibitive for general clinical use. Instead, they are mostly used for research. Fat mass can be measured indirectly using water (underwater weighing) or air displacement (BODPOD), or bioimpedance analysis (BIA). Each of these methods estimates the proportion of fat or non-fat mass and allows calcutation of percent body fat. Of these, BODPOD and BIA are often offered through fitness centers and clinics run by obesity medicine specialists. However, their general use in the care of patients who are overweight and with obesity is still limited. Interpretation of results from these procedures may be confounded by common conditions that accompany obesity, especially when fluid status is altered such as in congenstive heart failure, liver disease, or chronic kidney disease. Also, ranges for normal and abnormal are not well established for these methods and, in practical terms, knowing them will not change current recommendations to help patients achieve sustained weight loss.

Body Mass Index

Body mass index allows comparison of weights independently of stature across populations. Except in persons who have increased lean weight as a result of intense exercise or resistance training (e.g., bodybuilders), BMI correlates well with percentage of body fat, although this relationship is independently influenced by sex, age, and race ( 17 ). This is especially true for South Asians in whom evidence suggests that BMI-adjusted percent body fat is greater than other populations ( 18 ). In the United States, data from the second National Health and Nutrition Examination Survey (NHANES II) were used to define obesity in adults as a BMI of 27.3 kg/m 2 or more for women and a BMI of 27.8 kg/m 2 or more for men ( 19 ). These definitions were based on the gender-specific 85 th percentile values of BMI for persons 20 to 29 years of age. In 1998, however, the National Institutes of Health (NIH) Expert Panel on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults adopted the World Health Organization (WHO) classification for overweight and obesity ( Table 1 ) ( 20 ). The WHO classification, which predominantly applied to people of European ancestry, assigns increasing risk for comorbid conditions—including hypertension, type 2 diabetes mellitus, and cardiovascular disease—to persons with higher a BMI relative to persons of normal weight (BMI of 18.5 - 25 kg/m 2 ) ( Table 1 ). However, Asian populations are known to be at increased risk for diabetes and hypertension at lower BMI ranges than those for non-Asian groups due largely to predominance of central fat distribution and higer percentage fat mass (see below). Consequently, the WHO has suggested lower cutoff points for consideration of therapeutic intervention in Asians: a BMI of 18.5 to 23 kg/m 2 represents acceptable risk, 23 to 27.5 kg/m 2 confers increased risk, and 27.5 kg/m 2 or higher represents high risk ( 21 , 22 ).

Classification of Overweight and Obesity by BMI, Waist Circumference, and Associated Disease Risk. Adapted from reference ( 20 ).

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Disease risk for type 2 diabetes, hypertension, and cardiovascular disease.

Increased waist circumference can also be a marker for increased risk even in persons of normal weight.

Fat Distribution (Central Obesity)

In addition to an increase in total body weight, a proportionally greater amount of fat in the abdomen or trunk compared with the hips and lower extremities has been associated with increased risk for metabolic syndrome, type 2 diabetes mellitus, hypertension, and heart disease in both men and women ( 23 , 24 ). Abdominal obesity is commonly reported as a waist-to-hip ratio, but it is most easily quantified by a single circumferential measurement obtained at the level of the superior iliac crest ( 20 ). For the practioner, waist circumference should be measured in a standardized way ( 20 ) at each patient’s visit along with body weight. The original US national guidelines on overweight and obesity categorized men at increased relative risk for co-morbidities such as diabetes and cardiovascular disease if they have a waist circumference greater than 102 cm (40 inches) and women if their waist circumference exceeds 88 cm (35 inches) ( Table 1 ) ( 20 ). These waist circumference thresholds are also used to define the “metabolic syndrome” by the most recent guidelines from the American Heart Association and the National Lipid Association (e.g., triglyceride levels > 150 mg/dL, hypertension, elevated fasting glucose (100 – 125 mg/dL)) or prediabetes (hemoglobin A1c between 5.7 and 6.4%) ( 25 , 26 ). Thus, an overweight person with predominantly abdominal fat accumulation would be considered “high” risk for these diseases even if that person does not meet BMI criteria for obesity. Such persons would have “central obesity.” It is commonly accepted that the predictive value for increased health risk by waist circumference is in patients at lower BMI’s (< 35 kg/m 2 ) since those with class 2 obesity or higher will nearly universally have waist circumferences that exceed disease risk cut-offs.

However, the relationships between central adiposity with co-morbidities are also a continuum and vary by race and ethnicity. For example, in those of Asian descent, abdominal (central) obesity has long been recognized to be a better disease risk predictor than BMI, especially for type 2 diabetes ( 27 ). As endorsed by the International Diabetes Federation ( 28 ) and summarized in a WHO report in 2008 ( 29 ), different countries and health organizations have adopted differing sex- and population-specific cut offs for waist circumference thresholds predictive of increased comorbidity risk. In addition to the US criteria, alternative thresholds for central obesity as measured by waist circumference include > 94 cm (37 inches) and > 80 cm (31.5 inches) for men and women of European anscestry and > 90 cm (35.5 inches) and > 80 cm (31.5 inches) for men and women of South Asian, Japanese, and Chinese origin ( 28 , 29 ), respectively.

• EPIDEMIOLOGY OF OVERWEIGHT AND OBESITY IN THE UNITED STATES

In the United States (US), data from the National Health and Nutrition Examination Survey using measured heights and weights shows that the steady increase in obesity prevalence in both children and adults over the past several decades has not waned, although there are exceptions among subpopulations as described in greater detail below. In the most recently published US report (2017-2020), 42.4% of adults (BMI ≥ 30 kg/m 2 ) ( 30 ) and 20.9% of youth (BMI ≥ 95 th percentile of age- and sex-specific growth charts) ( 31 ) have obesity, and the age-adjusted prevalence of severe obesity (BMI ≥ 40 kg/m 2 ) was 9.2% ( 30 ) ( Figure 1 ).

Trends in age-adjusted obesity (BMI ≥ 30 kg/m 2 ) and severe obesity (BMI ≥ 40 kg/m 2 ) prevalence among adults aged 20 and over: United States, 1999–2000 through 2017–2018. Taken from reference ( 30 ).

Obesity and Severe Obesity in Adults: Relationships with Age, Sex, and Demographics

Age-Adjusted Prevalence of Obesity and Severe Obesity in US Adults. National Health and Nutrition Examination Survey data, prevalence estimates are weighted and age-adjusted to the projected 2000 Census population using age groups 20-39, 40-59, and 60 or older. Significant linear trends (P < .001) for all groups except for obesity among non-Hispanic Black men, which increased from 1999-2000 to 2005-2006 and then leveled after 2005-2006. Data taken from reference ( 31 ).

On average, the obesity rate in US adults has nearly tripled since the 1960’s (Reference ( 32 ) and Figure 2 ). These large increases in the number of people with obesity and severe obesity, while at the same time the level of overweight has remained steady ( 32 , 33 ), suggests that the “obesogenic” environment is disproportionately affecting those portions of the population with the greatest genetic potential for weight gain ( 34 ). This currently leaves slightly less than 30% of the US adult population as having a healthy weight (BMI between 18.5 and 25 kg/m 2 ).

Men and women now have similar rates of obesity and the peak rates of obesity for both men and women in the US occur between the ages of 40 and 60 years ( Figures 2 and 3 ). In studies that have measured body composition, fat mass also peaks just past middle age in both men and women, but percent body fat continues to increase past this age, particularly in men because of a proportionally greater loss in lean mass ( 35 - 37 ). The menopausal period has also been associated with an increase in percent body fat and propensity for central (visceral) fat distribution, even though total body weight may change very little during this time ( 38 - 41 ).

The rise in obesity prevalence rates has disproportionately affected US minority populations ( Figure 2 ). The highest prevelance rates of obesity by race and ethnicity are currently reported in Black women, native americans, and Hispanics ( Figure 2 and reference ( 42 )). In general, women and men who did not go to college were more likely to have obesity than those who did, but for both groups these relationships varied depending on race and ethnicity (see below). Amongst women, obesity prevelance rates decreased with increasing income in women (from 45.2% to 29.7%), but there was no difference in obesity prevalence between the lowest (31.5%) and highest (32.6%) income groups among men ( 43 ).

Prevalence of obesity among adults aged 20 and over, by sex and age: United States, 2017–2018. Taken from reference ( 30 ).

The interactions of socieconomic status and obesity rates varied based on race and ethnicity ( 43 ). For example, the expected inverse relationship between obesity and income group did not hold for non-Hispanic Black men and women in whom obesity prevelance was actually higher in the highest compared to lowest income group (men) or showed no relationship to income by racial group at all (women) ( 43 ). Obesity prevalence was lower among college graduates than among persons with less education for non-Hispanic White women and men, Black women, and Hispanic women, but not for Black and Hispanic men. Asian men and women have the lowest obesity prevelance rates, which did not vary by eduction or income level ( 43 ).

Central Obesity

As discussed above, central weight distribution occurs more commonly in men than women and increases in both men and women with age. In one of the few datasets that have published time-trends in waist circumference, it has been shown that over the past 20 years, age-adjusted waist circumferences have tracked upward in both US men and women ( Figure 4 ). Much of this likely reflects the population increases in obesity prevelance since increasing fat mass and visceral fat track together ( 52 ).

Age-adjusted mean waist circumference among adults in the National Health and Nutrition Examination Survey 1999-2012. Adapted from ( 51 ).

Childhood obesity is a risk factor for adulthood obesity ( 44 - 46 ). In this regard, the similar tripling of obesity rates in US youth (ages 2-19 years old) ( Figure 5 ) to 20.9% in 2018 ( 31 ) is worrisome and will contribute to the already dismal projections of the US adult population approaching 50% obesity prevelance by the year 2030 ( 47 ). Obesity prevalence was 26.2% among Hispanic children, 24.8% among non-Hispanic Black children, 16.6% among non-Hispanic White children, and 9.0% among non-Hispanic Asian children ( 48 ). Like adults, obesity rates in children are greater when they are live in households with lower incomes and less education of the head of the household ( 49 ). In this regard, these obesity gaps have been steadily widening in girls, whereas the differences between boys has been relatively stable ( 49 ).

Trends in obesity among children and adolescents aged 2–19 years, by age: United States, 1963–1965 through 2017–2018. Obesity is defined as body mass index (BMI) greater than or equal to the 95th percentile from the sex-specific BMI-for-age 2000 CDC Growth Charts. Taken from reference ( 50 ).

With regard to socieconomic status, the inverse trends for lower obesity rates and higher income and education (of households) held in all race and ethnic origin groups with the following exceptions: obesity prevalence was lower in the highest income group only in Hispanic and Asian boys and did not differ by income among non-Hispanic Black girls ( 49 ).

• INTERNATIONAL TRENDS IN OBESITY

Historically, international obesity rates have been lower than in the US, and most developing countries considered undernutrition to be their topmost health priority ( 53 ). However, international rates of overweight and obesity have been rising steadily for the past several decades and, in many countries, are now meeting or exceeding those of the US ( Figure 6 ) ( 54 , 55 ). In 2016, 1.3 billion adults were overweight worldwide and, between 1975 to 2016, the number of adults with obesity increased over six-fold, from 100 million to 671 million (69 to 390 million women, 31 to 281 million men) ( 54 ). Especially worrisome have been similar trends in the youth around the world ( Figure 6 ), from 5 million girls and 6 million boys with obesity in 1975 to 50 million girls and 74 million boys in 2016 ( 54 ), as this means the rise in obesity rates will continue for decades as they mature into adults.

The growth in the wordwide prelance of overweight and obesity is thought to be primarily driven by economic and technological advancements in all developing societies ( 56 , 57 ). These forces have been ongoing in the US and other Western countries for many decards but are being experienced by many developing countries on a compressed timescale. Greater worker productivity in advancing economies means more time spent in sedentary work (less in manual labor) and less time spent in leisure activity. Greater wealth allows the purchase of televisions, cars, processed foods, and more meals eaten out of the house, all of which have been associated with greater rates of obesity in children and adults. More details and greater discussion of these issues can be found in Endotext Chapters on Non-excercise Activity Thermogenesis ( 58 ) and Obesity and the Environment ( 9 ).

Regardless of the causes, these trends in global weight gain and obesity are quickly creating a tremendous burden on health-care systems and cost to countries attempting to respond to the increased treatment demands ( 59 ). They are also feuling a rise in global morbity and mortality for chronic (non-communicable) diseases, especially for cardiovascular disease and type 2 diabetes mellitus, and especially in Asian and South Asian populations where rates of type 2 diabetes are currently exploding ( 15 , 60 - 63 ). Efforts need to be made to deliver adequate health care to those currently with obesity and, at the same time, find innovative and alternative solutions that allow economies to prosper and to incorporate technologies that will reverse current trends in obesity and obesity-related complications.

Trends in the number of adults, children, and adolescents with obesity and with moderate and severe underweight by region. Children and adolescents were aged 5–19 years. (Taken from ( 54 )).

Obesity is both a chronic disease in its own right and a primary contributor to other leading chronic diseases such as type 2 diabetes, dyslipidemia, hypertension, and cardiovascular diseases. In the clinic, obesity is still best defined using commonly available tools, including BMI and waist circumference; although it is hoped that newer imaging modalities allowing more precise quantification of amount and distribution of excess lipid depots will improve obesity risk assessment. The general rise in obesity taking place in the US over the past 50 years is now occurring globally. In the US, the prevalence rates of obesity in adult men and women are now similar at 40%, and minorities are disproportionately affected, including Blacks, Native Americans, and Hispanics, with obesity rates of 50% or higher. Particularly worrisome is the global increase in obesity prevalence in children and adolescents as these groups will continue to contribute to a rising adult obesity rates for several decades to come. As important as finding solutions that address the global logistical and financial challenges facing health-care systems attempting to meet current demands of obesity and weight-related co-morbidities will be finding innovative solutions that prevent and reverse current population weight gain trends.

This electronic version has been made freely available under a Creative Commons (CC-BY-NC-ND) license. A copy of the license can be viewed at http://creativecommons.org/licenses/by-nc-nd/2.0/ .

• Cite this Page Purnell JQ. Definitions, Classification, and Epidemiology of Obesity. [Updated 2023 May 4]. In: Feingold KR, Anawalt B, Blackman MR, et al., editors. Endotext [Internet]. South Dartmouth (MA): MDText.com, Inc.; 2000-.

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Computer Science > Computation and Language

Title: realm: reference resolution as language modeling.

Abstract: Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user's screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in reference resolution, particularly for non-conversational entities, remains underutilized. This paper demonstrates how LLMs can be used to create an extremely effective system to resolve references of various types, by showing how reference resolution can be converted into a language modeling problem, despite involving forms of entities like those on screen that are not traditionally conducive to being reduced to a text-only modality. We demonstrate large improvements over an existing system with similar functionality across different types of references, with our smallest model obtaining absolute gains of over 5% for on-screen references. We also benchmark against GPT-3.5 and GPT-4, with our smallest model achieving performance comparable to that of GPT-4, and our larger models substantially outperforming it.

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